Amit Mitra
email: amit.mitra@uwe.ac.uk

Nicholas O'regan

Ximing Ruan

Rays of Uniqueness -Cloud in Multinational Organisations: Challenge to Traditional Business System Alignments

Keywords: IS strategy, Business strategy, IS assets, Cloud Computing, Resource sharing 1

Cloud computing initiatives support multinational corporations in optimizing resource utilisation and at the same time provide ubiquitous capacity to satisfy expectations of users within it. However, research carried out in the early 1990s demonstrated consequences when there were mismatches between business and IT strategies. The advent of utility computing through cloud based resource development seems to have altered both the perception of IT resources as well as the expectations of their use. In this paper by using a resource based view of cloud computing we examine the nature of unique capacity development in multinational organisations. Evidence for the research was gathered through interviews conducted in two well-known multinational companies in the oil and natural gas and the car manufacturing sectors respectively. The lessons drawn in this research is likely to be beneficial for organizations implementing Cloud based solutions.

Introduction

Although cloud computing is a term associated with a new innovative development in the world of computing yet the idea of sharing resources originated in the 1960s and 70s [START_REF] Waschke | Cloud Standards: Agreements that hold together clouds[END_REF]. At the same time the contexts of the two developments couldn't be more different. While computing used to be a scarce resource in the 1960s and 1970s it is not so now with the advent of PCs for end users as well as the ubiquitous enabling role played by the Internet. Cloud computing and time sharing are connected to the notion that computing resources can be used as a utility that is distributed as a service like electricity and water. Evidently, cloud computing characterises a fundamentally different way of invention, development, deployment, scaling, updating, paying for information and communication services [START_REF] Marston | Cloud computing -The business perspective[END_REF]. Many organisations that are considering moving onto the cloud are initially challenged by the notion of billing at point of use and subsequently by the whole changes to decision making that this sort of service orientation creates. Adoption of core technologies like cloud can also have influence on organisational adaptations as reported in the context of management innovations [START_REF] Khanagha | Management innovation and adoption of emerging technologies: The case of cloud computing[END_REF]. [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] found [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] duality of impact to be a key feature of technology adaptation processes within British local government. As surplus capacity and re-use or sharing of resources becomes the prevalent obtaining reality, the sharing features of cloud computing will be increasingly an attractive arrangement of choice.

In the quest to estimate cloud use effectiveness it seems obvious that the resource based view (RBV) of the firm would be a credible analytical approach. RBV is based on the notion that organisations succeed in gaining competitive advantage by using its assets as well as sustained competitive advantage is gained by using its existing capabilities. According to [START_REF] Wade | The resource based view and IS research: Review, extension, and suggestions for future research[END_REF] [START_REF] Wade | The resource based view and IS research: Review, extension, and suggestions for future research[END_REF] Information system resources of firms may be visualised through a couple of categories that include IS assets (technology based) and IS capabilities (systems based). IS assets have an inherent disadvantage in that they can be easily copied by competitors and therefore is fragile with regard to sustainability of competitive advantage [cf. [START_REF] Leonard-Barton | Core capabilities and core rigidities: A paradox in managing new product development[END_REF][START_REF] Teece | Dynamic Capabilities and Strategic Management[END_REF]. An important issue for this paper is the fact that there is growing evidence to show that competitive advantage often depends on the firm's superior deployment capabilities [START_REF] Christensen | Meeting the challenge of disruptive change[END_REF][START_REF] Day | The capabilities of market driven organisations[END_REF]. It is clear that certain key criticisms of the RBV [START_REF] Kraaijenbrink | The resource based view: A review and assessment of its critiques[END_REF] have stemmed from the static assumptions of the nature of resource, value and sustainable competitive advantage, for the purposes of this paper we'd like to presume both dynamic settings and unique capabilities enable organisations to develop sustainable competitive advantages.

Multinational companies constantly seek to develop unique inimitable capacity so that they can create competitive advantage over their competitors, despite the fact that initiatives for changes are difficult to implement due to resistance.. MNCs that succeed in implementing change by overcoming such resistance usually are supported by leadership that has foresight, is able to handle risk at the same time become considerably unique in the way their business systems operate [START_REF] Mitra | Visions of a pole position: Developing inimitable resource capacity through enterprise systems implementation in Nestlé[END_REF]. Such an orientation as for instance in the study conducted within Nestlé by Mitra and Neale (2014) [START_REF] Mitra | Visions of a pole position: Developing inimitable resource capacity through enterprise systems implementation in Nestlé[END_REF] is in contrast to findings which showed that co-ordinating IS plans with business plans impedes effective IS planning [START_REF] Lederer | Co-ordination of information systems plans with business plans[END_REF]. Despite many successful IS implementations yet instances of IS failure has lingered that has enabled the issue of alignment to be pre-eminent in the context of large MNCs. Just like [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] identified maturity to be a key parameter in Geographic Information Systems implementation within British local government similarly, [START_REF] Luftman | Assessing IT/Business alignment[END_REF] categorises [START_REF] Luftman | Assessing IT/Business alignment[END_REF] alignment to be dependent on six categories of maturity. It would be interesting to see if Hayward's Rigs and Nihon Motors are able to look more closely at how cloud computing can support individual processes [START_REF] Tallon | A process-oriented perspective on the alignment of information technology and business strategy[END_REF] rather than how it can support an entire strategy.

2.

Nature of cloud based resource capabilities

In a context where overwhelming evidence shows [START_REF] Bharadwaj | A resource based perspective on information technology capability and firm performance: an empirical investigation[END_REF][START_REF] Mitra | Visions of a pole position: Developing inimitable resource capacity through enterprise systems implementation in Nestlé[END_REF] that IS capacity development leads to inimitable resource advantages, it is clear that both Hayward's Rigs and Nihon Motors did not doubt the possibility of developing cloud based capacities. At the same time maturity of using competencies can make a difference to the way organisations eventually acquire competitive advantages. In the study [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] on British local government based implementations of geographic information systems, [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] found that levels of maturity in IS use usually leads to a couple of distinctly different formats in which organisations develop IS capacity. According to [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] adoption and adaptation of IS may be manifestly linked to maturity levels of the organisation's implementations of specific IS. Indeed organisations that strive to adapt also seek alignment between businesses and IS strategies for acquiring competitive advantages. Successful alignment of business and IS objectives is a 'process of continuous adaptation and change' [START_REF] Hirschheim | Detours in the path toward strategic information systems alignment[END_REF]. In this study, adoption of cloud computing was never in question. However, Hayward's Rigs and Nihon Motors went through varied adaptations of cloud use to garner specific capacities. Following Armbrust et al (2010) [START_REF] Armbrust | A view of cloud computing[END_REF], cloud computing in this paper would refer to both the applications delivered as services over the Internet and the hardware and systems software within data centres that provide those services. Alignment of business strategy and information systems strategy has been a longstanding research pivot around which various organisations and their ISs have been explored. It must be borne in mind that the notion of introducing cloud computing is probably somewhat different in contrast to traditional systems. Whilst [START_REF] Bharadwaj | A resource based perspective on information technology capability and firm performance: an empirical investigation[END_REF] implies [START_REF] Bharadwaj | A resource based perspective on information technology capability and firm performance: an empirical investigation[END_REF] that IT capability is a rent generating resource that is not easily imitated or substituted in large companies yet, Armbrust et al (2010) have clarified [START_REF] Armbrust | A view of cloud computing[END_REF] that pay as you go as used in cloud computing is clearly tied to usage. Renting usually involves paying a negotiated amount over a fixed period of time irrespective of use. Pay as you go involves metering usage and charging based on actual usage, independently of the time period over which the usage occurs. With the advent of cloud computing, this is perhaps a key difference that has come about in the estimation of IT resources. Dwelling on scale and simplicity as the new dimensions that cloud brings to the context of multinational companies, Grossman (2009) concurs [START_REF] Grossman | The case for cloud computing[END_REF], that pay as you go to use cloud capacity is a facet that has hitherto remained unknown. In the context of MNCs as operations scale to international contexts using multiple proprietary providers could lead to challenges in application of security policy [START_REF] Jaeger | Cloud computing and information policy: Computing in a policy cloud?[END_REF]. Reductions in budgets and higher space requirements as computing becomes more web based, there is a compulsion that is driving large organisations to rethink their current capacity provisioning [START_REF] Sarkar | Sailing the cloud: A case study of perceptions and changing roles in an Australian University[END_REF]. Cloud obviously provides a veritable option that is increasingly being taken seriously by large organisations.

Methodology adopted

Data were collected in two multinational companies that have significant operations within the UK. The first is a British multinational that is well known in the oil and natural gas sector. The second is a Japanese car manufacturer that has significant UK based manufacturing capacities. Prevalent non-disclosure conditions do not permit us to use either the names of the organisations or the staff who have been interviewed for the study. For the purposes of this paper the companies will be referred to using pseudonyms as Hayward's Rigs (HR) and Nihon Motors (NM) respectively. Both companies have assets and personnel spread across various local and global locations that need to seamlessly interact for efficient delivery of business. It is clear that further to their experiences, both companies have realised that merely transferring all of their data across to cloud based repositories is unlikely to work [START_REF] Runciman | The IT Linguist, ITNOW[END_REF]. There were also documents that were used to enrich the analysis that is referred to in this paper. Whilst both companies have been considering using some cloud services ever since 2007 yet faced with various local and global challenges both companies had embarked around 2010 to seriously integrate cloud computing into their operational needs.

A case study approach [START_REF] Yin | Case study research: Design and methods[END_REF] was used to collate evidence on HR and NM.

Table 1: Antecedents of cloud use Dimension Hayward's Rigs Nihon Motors Industry expectations

Increased demand from business managers to deploy functionality quickly. HR executives, like many of their counterparts in other organisations, are increasingly "tech savvy"; that is to say they are becoming more alert to the possibilities that developments in technology offer and are keen to utilise these technologies to improve organisation performance. Cloud services are attractive because they allow organisations to seize opportunities quickly and "strike whilst the iron is hot".

Although Nihon Motors began exploring cloud based solutions ever since 2008 yet the need for websites to load swiftly and address customer expectations was a key driver. Personnel within Nihon Motors viewed reliance on cloud services would provide a use and dispose advantage as their envisaged web services could be located in external clouds that needn't be integrated with the rest of the company's operations. In a business where there is always a possibility of disruptions to the supply chain (as, for example, the earthquake in Japan highlighted) or the need for parts recall (as, for example, in the recent recalls of cars by Toyota due to airbag faults) providing customers and collaborators with up-to-date information is critical. Cloud technologies have the potential to provide more flexible, efficient and effective communication channels.

Scalability

In the oil and gas industry, as the recent Gulf of Mexico incident vividly demonstrated, it is important to be able to scale up capacity at very short Host providers have far bigger Internet pipelines than individual firms and flexible charging mechanisms make this provision particularly attractive to firms notice to deal with unforeseen events. In addition one-off activities, for example HR's involvement with the Olympic games or its 'commitment to America' advertising campaign, require temporary increases in information and communications capacity. Evidence suggests that flexibility is achieved more easily and cost-effectively through the Cloud.

facing intermittent surges in activity. To quote, "We could have put in a much bigger pipe to the Internet but that would have been a big pipe that was only used one percent of the time and was unlikely to be cost effective." The car industry places importance on green factors within its procurement processes. For NM, in particular, it is important that it produces its cars in as environmentally friendly a way as possible. Whilst the main emphasis within the company is on 'greening' it's manufacturing plant, NM as a whole strives to be greener where ever it can and this includes its provision of computing services. Cloud computing delivers environmental benefits to NM in a number of ways. First, the economies of scale available to specialist cloud service providers implies that it is in these vendors' interest to incorporate sophisticated, eco-friendly features into the design and operation and their data centres.

Investment

Focus on core capacities

As the pace of development in information and communication technology speeds up so it becomes more difficult for IT departments to keep up with latest developments in all fields. Attempting to maintain leading edge knowledge in areas that support rather than constitute the core business can be a distraction to Whilst it is possible to develop equivalent capacity and capability in-house, the experience NM gained through its tendering processes suggests that specialist providers offer much cheaper solutions because of they can exploit economies of scale, scope and learning through the re-use of knowledge. Further, cloud vendors offer enhanced search capabilities that can be bought off-the-shelf. managers' attention. Handing over responsibility for noncore activities to specialists is likely to produce better results.

This enables firms like NM to improve the performance of websites from the end-users perspective quickly and relatively cheaply.

4.

Scenario at Hayward's Rigs

Moving email services to the cloud

One of the key decision parameters for HR was the extent to which the email services it was seeking to procure needed to be customised to HR's specific organisational context. On the one hand the company sought to get the economic and commercial benefits associated with standardised Cloud offers, but on the other hand, recognise that the nature and complexity of its business required some significant elements of custom-build. As one of our respondents explained, "it became a conversation about where we wanted to get Cloud economics and Cloud commercials but actually the reality was that it always looked like more of a custom-built environment." Following a number of detailed rounds of discussion and negotiation, the contract was eventually awarded to T Systems, the corporate customer division of Deutsche Telekom in July 2012. Under the terms of the contract T Systems will provide a secure private cloud which will enable HR's 830,000 plus employees around the world to access email services from a range of mobile and computer devices. The contract is for a five-year period and is based on a "pay-per-use" model.

HR's exploration of moving an information management platform to the Cloud

HR's exploration of this option has followed a similar path to that of email services in terms of the procurement process. It issued a request for information (RFI) to test the market and to identify potential suppliers and entered into discussion with some of the dominant players like Amazon Web Services (AWS). These large providers of public Cloud services have presented HR with a new and rather unexpected set of challenges. Providers like AWS provide a standardised service that takes little account of the size or nature of the purchasing organisation. As one of our respondents explained:

" you can forget trying to have a conversation with Amazon in the way we used to with Hewlett Packard (HP) where they (HP) will take on certain service levels and undertake to do special things for you as a customer. No chance [with Amazon]. HR as an organisation has as much firepower with Amazon as I have as an individual customer. It makes no difference whatsoever to them so you need a different set of levers to manage your risk exposure around that and that leads you actually to a different … architecture and a completely different dynamic."

As this quote demonstrates the balance of power between provider and purchaser is currently very different from that which large multinationals have come to expect. HR, like most other global organisations, is used to having a degree of bargaining strength in its negotiation with suppliers but the dynamics of public Cloud services are unusual. A few large players dominate segments of the newly emerging Cloud industry and these first movers have been able to exploit the economies of scale and experience to bring costs down, tipping the balance of advantage in their favour.

Strategic implementation at HR

Moves to Cloud-based provision can seem to be relatively inconsequential from the end-users perspectives but from a broader organisational perspective, they can have far-reaching and unexpected effects. HR managers, for example, have found that the exploration of Cloud-based solutions has required them to rethink many of their existing practices and processes. Executives involved in the decision-making process report that there is a potential impact on everything from legal frameworks, through billing and charging for IT services to the way performance is measured.

From the end-users point of view a change in the provision of email services or an information management platform can go almost unnoticed but the adoption of cloud services means that "every decision has a new complexion to it". For example in terms of legal contracts, HR's legal teams are used to negotiating specific clauses in contracts but in a "multi-tenant" environment that doesn't make sense and Amazon, for example, requires its customers to adopt standard terms and conditions. The adoption of standard terms and conditions has knock-on implications for the way risks are managed. For instance if AWS went down, how would disaster recovery be managed in this new environment? Similarly, the fact that charges for cloud services are consumption-based means that the way IT services are billed for internally, needs to be altered to reflect this and incentives need to be in place to create economies on the "consumption" of chargeable services. As a respondent put it: "it's like a prism -take the example of how we charge for IT internally -here we have a specific way of charging the business for services which is not hour by hour/consumption based, it is pretty much year by year consumption. So you can have a situation where, by the very nature of the Cloud, you can peak, move in, see lots of different things and your internal charging models encourage that level of use and variability but that isn't an advantage anymore, that's a problem. So it's how you begin to start to chip away at a series of financial mechanisms of governance that might have been in place for twenty years and … there are lots of dead bodies in stuff like that. You have to work your way through (myriads of issues like this) to begin to leverage the advantage that this other thing (the Cloud) gives."

The same sentiment is expressed in the following comments and illustrates how difficult it is to implement changes that on the surface can appear quite modest but have wide-scale implications.

"When you are trying to create a business case for this (Cloud), You're having to force fit a new world model into an old world model so you try to explain the new world model in old world terms and those things are not natural bedfellows. So even trying to -apples for apples-make financial comparisons can be difficult."

"It took me a long time to actually get to grips with the change. You can intellectually understand it quite quickly but to sort of emotionally buy what you are being told and really begin to deeply understand how to might actually do that [implement a transition to Cloud provision] takes a bit more time."

Cloud led competitive advantage at HR

Cloud solutions, for example, allow HR to do the things it has always done more speedily. For instance a successful national marketing campaign could be scaled up globally very rapidly or ERP systems could be deployed in ways that achieve further cost savings but the "commodified" nature of the cloud means that adoption of cloud solutions is unlikely to be allowed to encroach into areas of core expertise. HR's has distinctive capabilities in finding new oil and gas reserves, in geo-space analysis and in many other areas that are supported by high-performance computing environments. Strategically it is important that HR retains and develops its knowledge of crucial technologies. Cloud-based computing will allow HR to cut costs, be quicker to market and stay at the leading edge of support technologies so it is perceived to be an important tool that HR needs to deploy but not something that will allows HR to differentiate itself from competitors. In other words from HR's perspective the ability to deploy cloud computing solutions is a very important threshold capability but not a distinctive one.

Scenario at Nihon Motors

First move to the cloud

Having made the decision, in principle, to move to a cloud-based solution for its websites, NM selected its provider through a traditional tendering process. It approached around five vendors who were known leaders in the web-hosting marketplace and put out a Request for Information (RFI). The RFIs paved the way for formal tenders that were evaluated using NM's usual internal protocols and scoring systems. The contract was awarded to a vendor who had the advantage of being located in the Thames Valley close to NM's own offices and the stand-by site was in London's Docklands.

NM's subsequent cloud ventures

NM's next major cloud venture accompanied its launch of its sporty, hybrid car, the BS-Y. The marketing team responsible for the launch was keen to utilize the power of social media and came up with the concept of Mode Art. Mode Art was described as a web-site and Facebook application which turned the user's life into art. User information was pulled from Facebook and then merged into a unique art composition based on one of the NM BS-Y's driving modes, namely Sport, Urban or Economy.

The resulting artwork could be shared on Facebook, sent to friends or downloaded on to a mobile phone and was designed to act as a catalyst for viral marketing. The challenge for IT was to provide sufficient capacity for storing and processing users' images, particular given there was considerable uncertainty about possible uptake.

The ICT team supporting this launch decided to buy cloud-based storage capacity from Amazon, paying for it by gigabyte per month depending on utilization. This enabled the company to put storage capacity in place in a matter of days and also had the advantage of allowing the company to specify where its data was to be stored, hence avoiding some of the legal issues concerning data protection that can be very complex when data is stored off-shore. Purchasing data storage from Amazon was, however, a commodity transaction and, at the time, Amazon was not geared to corporate customers as our respondent's experience vividly illustrated:

"Amazon weren't geared to corporate buying. It actually ended up with me paying [for data storage] on my own corporate credit card. This was the only way we could do it because they weren't in a position to corporate purchase orders or to invoice the company."

Whilst NM as the purchaser had to accept Amazon's standard terms and conditions and had to navigate a payment system designed for individuals rather than corporates, the big benefit was in terms of price. As it turned out the take-up for this campaign was much lower than expected but the low sunk costs meant that the failure of this particular marketing experiment contained as the following quote illustrates: "… it does illustrate one of the great benefits of the Cloud in that this particular marketing campaign was not successful in terms of attracting people… But the great advantage was that I was paying something like 5 pence per month for the storage we got from Amazon whereas if we tried to provision it internally, we would have put, I don't know maybe a hundred gigs of storage or so behind it and we would have had to pay for it, provision it etc. ---you would probably have been talking at least five to ten thousand pounds of infrastructure. … The flexibility of Cloud storage and Cloud computing can give you some substantial cost advantages."

Whilst in NM's case its marketing teams had always been urged to take a creative approach to new product launches and were encouraged to take calculated risks, the changes in the cost structure associated with cloud-based web provision mitigates against downside risks and facilitates experimentation.

The choice between private and public cloud solutions required NM to engage with some difficult trade-offs. On the one hand buying off -the-shelf public cloud services provided by firms like Amazon offered significant cost savings and flexible capacity but it also meant that NM still had to do a large amount of work in-house because they were "just buying the infrastructure rather than the solution." On the other hand the specialist providers like Rackspace offered high-end services and "get web-sites up and running quickly with little effort [on the buyer's part]" but highly customized solutions are expensive. In the end NM went for a middle of the road solution that involved some degree of customization.

"The company that runs the IT helpdesk for NM (Europe) has its systems based in India. That involved getting data protection agreements signed with all the NM companies in Europe to say we approve employee data being held in systems in India. It's just a headache."

In terms of cost savings, the move to a quasi-public cloud was estimated by our respondent to "result in a 30% reduction in annual operating costs and … to deliver a better solution. … They call it the virtual team but if you look at the people that they [Phoenix] have supporting the web-site added to the people we had internally supporting the web-site there is definitely a higher level of support."

Strategic implementation at NM

One of the concerns commonly expressed about moves to cloud-based solutions is that there may be resistance to these kinds of developments from in-house IT staff, in part because the move to the cloud has the potential to reduce employment opportunities. This does not appear to have been an issue at NM because there were more than enough new projects continuously coming on stream to fully deploy the existing staff's time and expertise. In addition, many of the capabilities required did not exist in-house.

"Computing is an area where there are always new technologies and new projects and you've got to decide where you are going to put your people. If you look at the skills required we have never had those in-house. Yes, we ran virtual servers in-house but running virtual servers between two sites that requires a level of N-ware expertise that NM never really had in-house and would struggle to afford having in-house. We get 24/7 monitoring from the provider but if that was provisioned in-house we'd have to put our people on to shift systems which we can't do and we want the system to be scalable on demand so that the website automatically adds capacity if there are peaks in usage."

Cloud led competitive advantage at NM

Our respondent drew parallels between cloud computing technologies and outsourcing. In just the same way as it is not sensible for a firm to outsource activities that were the basis of its competitive advantage so it is not prudent for a firm to move computing activities to the Cloud if those activities formed part of the organisation's distinctive capabilities. To quote our respondent:

"The only way Cloud computing helps in delivering competitive advantage is in a secondary way. Cloud computing can give you cost and speed market advantages so if part of your competitive advantage is getting to market quickly then Cloud computing can help. But, if you view computing in its own right as your competitive advantage then you don't outsource it because, by definition, you're using public things that people can easily copy, easily reproduce and so it very quickly doesn't become your competitive advantage anymore."

Looking to the future, however, it is likely that Cloud computing solutions will take on increasing significance for the car industry. There is a trend towards cars becoming network nodes in their own right -that is to say more information and communication technology being incorporated into vehicles so that cars are permanently online from the manufacturer's perspective. Whilst all car producers would like to gain an advantage by exploiting the opportunities that 'always online' cars potentially offer, it seems unlikely that a single car manufacturer could afford to invest the sums of money necessary to build unique systems and distinctive advantages.

"We'd very much like to have a competitive advantage but, realistically, we probably can't because the cost of provisioning an 'always online' car and having the nationwide networks to do that is well outside the scope of a single car producer. We will probably have to collaborate with mobile network providers. We're going to be using other people to help us and we will need to tap into publicly available services so that is never going to be our competitive advantage. Our competitive advantage will have to be closer to home, for example by designing the interfaces, helping people to use the features of the car and so on."

The fact that Cloud solutions are ubiquitous and easily replicable means that careful consideration does need to be given to which activities are transferred to the Cloud. In the case of CRM and data mining, for example, our respondent was of the opinion that the interrogation of customer data is best done in-house.

"[Interrogating customer data] and data mining is difficult is some ways. You are handling large volumes of data and, yes, that could be a candidate for the Cloud but then you look at the tools you need to handle those large volumes and to what extent are they Cloud-based? You can't shift large data over the network or the Internet. It needs to be closer to home. Lots of the data-mining people are now doing in-memory computing, holding databases in memory [to undertake their analysis]. If you're not careful you will erode your performance advantage by hosting remotely."

NM sees its core capabilities as located in its design of cars and in its manufacturing capabilities so it needs its computing capacity next to the production line. Whilst cloud solutions and external hosting are helping the company to improve the efficiency and effectiveness of its overall operation, its production-related computing activities are likely to remain firmly in-house.

Conclusion

It is clear that the motivations and expectations of both companies vary somewhat. At the same time both realise that there are specific advantages that they could garner by using cloud solutions. For instance, HR realises that they'd be able to cut costs, be quicker to market and stay at the leading edge of support technologies. However, HR doesn't consider cloud to be able to provide it with a capability that would allow it to differentiate itself from competitors. In contrast to the oil and natural gas sector, the car industry is quite heavily customer orientated. In such a context as was evident through the facebook exercise initiated by NM for its BS-Y model there are serious limitations on what can be achieved in-house. Further as hybrid cars become more network reliant, cloud based capacity may become an imperative. Probably the most fascinating outcome of the research for this study is embedded in the challenges being experienced by both MNCs in implementing cloud solutions. Both HR and NM have reported that decision making is getting affected as provisioning of computing resources becomes more commodified. Here the duality dimensions of influence as found by [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF] [START_REF] Mitra | An Interpretation of the Organisational Context of Geographic Information System use in British Local Government[END_REF]seems to become gradually evident as the companies move towards fully cloud orientated organisations.


Xicheng Fu

Mingcai Lin

Xingdong Ma

The Key Algorithm of the Sterilization Effectiveness of Pulsating Vacuum Sterilizer

Keywords: data mining, cluster analysis, the average quadratic interpolation 1

The relationship between the physical parameters of pulsating vacuum sterilizer and the piping system states is analyzed based on cluster analysis and interpolation approximation algorithm. The cluster analysis is performed first, followed by the interpolation approximation of the cluster data. The algorithm provides a comprehensive sterilization efficacy evaluation, as well a feasible method to analyze the states of the system.

Introduction

Pulsating vacuum sterilizer is mainly used in hospitals, chemical industry, food industry, and scientific research. It is also widely used in the sterilization of the routine items in medicinal product testing, vaccination, and bioengineering industries. Having replaced the exhaust sterilizer, pulsating vacuum autoclaves are most widely used in hospitals. However, the solenoid valves, the check valves and other valves in the exhaust piping system are prone to failure and need repeated maintenance due to the complexity of the structure of the pulsating vacuum sterilizer piping system, The safety of the pulsating vacuum sterilizer also is a key problem.

B-D Test

B-D tests are performed to assess the residual air in the pulsating vacuum sterilizer and steam penetration (Figure 1); it is specially designed to test the air exhaust effect of the pulsating vacuum pressure steam sterilizer. The batch challenge test (Process Challenge Device, PCD) is used to determine whether the entire load steam sterilization cycle has achieved the sterilization conditions, often need to release early using the biological indicators or the fifth class of chemical sterilization indicator. The study of sterilization parameters is the prerequisite of a small probability of surviving microbes.

Figure 1. B-D Test Program

It is often difficult to detect the microbes using the current sterility test method. Therefore, it is necessary to verify the reliability of the method of sterilization. The values of F and F0 can be used as the indicators to verify the reliability of the parameters of sterilization process.

Sterilization parameters are mainly: 1). (2-1) D is the time required to reduce the number of microbes to the one-tenth of the original number, or to reduce to one logarithmic units or decrease (lg100 reduce to lg10). The value of D depends on the different types of sterilization methods and different types of microbes.

2). Value of Z

Z is the value of temperature increment that is required to lower the value of D lg , i.e. sterilization time required to reduce to one tenth of the original number of microbes, or the temperature increment required to kill 99% of the microbes in the same time.

1 log 2 log / 1 2 D D T T Z    (2-1)
3). Value of F

The time required at the sterilization temperature (T) to achieve the same sterilization effect as with the reference temperature (T0), under a certain value of Z. It is commonly used in dry heat sterilization with the unit of min.

   10 t F (2-2)

4). Value of F0

The time that is required to sterilize at a temperature (T) with Z =10 o C to achieve the same sterilization effect at 121 o C with Z = 10 o C. F0 value is limited to autoclav-ing only. The biological F0 value is equivalent to the time needed to kill all the microbes at 121 o C during hot sterilization. F0 reflects the unity of sterilization temperature and time on the sterilizing effect. This value is more accurate and practical. A safety factor should be increased to ensure the sterilization effect, typically 50% more than the theoretical value in order to achieve the expected remaining number of microbes, i.e., the probability of contamination.

The value of physical F0 is defined as:

    Z T t F / 121 10 0 (2-3)
The value of biological F0 is defined as:

  Nt N C D F lg 0 lg 121 0     (2-4)
How to correctly and timely asses of the relationships between the operating conditions, such as solenoid valves, check valves in the piping system and the physical parameters that are associated with the sterilization effectiveness will be able to provide a feasible analysis algorithm for the equipment states. This paper constructs the correlation algorithm between the sterilization parameters and the state of the piping system based on the cluster analysis and numerical interpolation approximation.

Cluster Analysis

The concept of data mining (DM, Data Mining) was first proposed at the ACM annual meeting in 1995. It is the process of extracting implicit, undiscovered, and potentially valuable information from the database. Data mining is the product that IT has reached to a certain stage of development. It requires large-scale databases, efficient computing power, and effective calculation algorithms. Data mining is the process of extracting a useful knowledge from the large amount of data that stored in the database, data warehouse or other libraries.

According to the large number of historical data, cluster analysis is performed to determine the distribution of device parameters under different operating conditions. In this paper, the improved K-Means clustering algorithm is used.

The improved K-means clustering algorithm is discussed as the following: Method of selecting the initial cluster centers: [START_REF]ANSI/AAMI ST 79:2006 Comprehensive guide to steam sterilization and sterility assurance in health care facilities[END_REF]. Select the object that is the farthest from the mean point as the initial cluster centers first cluster (seed) O 1 ; (2). Select object O 2 , which is the farthest from object O 1 , as the initial center of the second cluster; (3). Calculate of the minimum distance between of the remaining unallocated objects and the selected the seeds, and find the maximum value among the minimum distances. The object corresponds to this maximum value is taken as the initial seed of the next cluster; (4). Repeat the process K times to achieve K points. These K points are the initial the initial cluster seeds. In this paper, K=3 is selected. The reason is that the status of the devices is always one of the following: qualified, sub-qualified and disqualified. The corresponding data will be presented in three different distributions. After digging out the three clustering results, the quadratic interpolation is performed with respect to sterilization temperature, sterilization time, etc., to determine the distribution of the three clusters of data which are representative of the state apparatus. Results of data mining are closely related to the types of the data selected. The result of the analysis and the actual experiment may vary greatly if the sample data is not comprehensive. Then we need to select new sample data for data mining.

4

Lagrange Interpolation

The Construction of the Key Algorithm of Equipment Status Analysis by Piecewise Average Quadratic Interpolation Based on Cluster Analysis

Improved K-Means Algorithm

K-Means clustering algorithm refers to the grouping the collection of physical or abstract objects into different clusters according to the similarities the objects.

Input: A database with N objects, the number of clusters K, with   1 , 0   Output: Clustering result, i.e., K clusters. Method: [START_REF]ANSI/AAMI ST 79:2006 Comprehensive guide to steam sterilization and sterility assurance in health care facilities[END_REF]. Select K initial cluster seeds according to the initial cluster centers shown previously;

(2). Repeat;

(3). Assign each object the most similar class according to the principle of the most similarity within the class (the closest distance between the clusters and the cluster seeds); (4). Calculate the minimum similarity (5). Select the data with the similarity greater than the threshold in the cluster

) 1 (  k t C
and the cluster seeds

) 1 (  k t O , to get the set of ) 1 (  k t CN ; (6)
. Calculate the mean value of

) 1 (  k t

CN

, thus being the cluster seed; [START_REF] Burden R L | Numerical Analysis[END_REF]. Repeat until there is no further change between the two adjacent cluster seeds.

Piecewise Average Quadratic Interpolation

The piecewise average quadratic interpolation will be constructed in this paper. The procedure of building the average quadratic interpolation is shown as the following:

(1) Partition the interval   b a, as: b x x x x a k         2 1 0 :
(2) Construct the Lagrange quadratic interpolation polynomial ) ( 2x p i over the in-

terval of   1 1 , ,   i i i x x x ; ) )( ( ) )( ( ) )( ( ) )( ( ) )( ( ) )( ( ) ( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 i i i i i i i i i i i i i i i i i i i i i i x x x x x x x x y x x x x x x x x y x x x x x x x x y x p                              (5-1)
Likewise, construct the Lagrange quadratic interpolation polynomial (5-4)

) ( 2 ) 1 ( x p i  over the interval   2 1 , ,   i i i x x x : ) )( ( ) )( ( ) )( ( ) )( ( ) )( ( ) )( ( ) ( 1 2 2 1 2 2 1 1 2 1 2 1 2 1 2 ) 1 (                               i i i i i i i i i i i i i i i i i i i i i i x x x x x x x x y x x x x x x x x y x x x x x x x x y x p (5-2) (3) Construct the interpolation polynomial over every interval of   1 ,  i i x x : 2 ) ( ) ( ) ( 2 ) 1 ( 2 x p x p x p i i i    (5-3) (4

Example of Relationship between the Sterilization Parameters and the Pipeline Equipment States

The data of 1000 pot experiments are collected using the XG1-DWED0.8 pulsating vacuum sterilizer from Shandong Xinhua Medical Instrument Corporation. The data is show as Figure 2. It is shown that the clustering analysis of the equipment operating status with respect to the F0 value using the improved K-means algorithm. The values of F0 for the three clusters, qualified F0 M , sub-qualified F0 N , and disqualified F0 L, are calculated by: The working states of the sterilization device can be automatically informed according to the clustering interpolation data curve, with pre-set threshold values of the piping system operating states.

  Nt N C D F lg 0 lg 121 0     (6-1)
In this example, the equipment operating status is analyzed using the parametric statistical method using the collected data, and good results are achieved. The automatic recording and the automatic notification before reaching the specified number of times are realized.

Conclusion

In summary, the operating states of the sterilization system and the status of the units can be analyzed using the algorithm proposed in this paper. Thus improve the quality of sterilization. It has ensured the supply of the sterile items in clinical and hospital operating rooms. It can also provide a practical algorithm to analyze system states for other equipments.

Figure 2 .

 2 Figure 2. The curve of sterilization temperature vs. sterilization timeThe Lagrange quadratic interpolation function can be constructed using multiple discrete data points according to equation. The values of F0 for the three clusters are taken as: F0 M for qualified state, F0 N for sub-qualified state, and F0 L disqualified state. Different values of F0 correspond to different operating states. The interpolation curve of disqualified F0 L according to the above algorithm is shown in Figure3. It shows the interpolation of F0 L when the sterilization steam trap vale malfunctions. The temperature drops below 132 o C because of the malfunction. The interpolation curve reflects the equipment state very well. In the mean time, the interpolation function is able to eliminate the interferences of different operating states so that the accuracy of the analysis is improved.

Figure 3 .

 3 Figure 3. The Interpolation Curve of F0 L

Acknowledgements:

The work of this paper is supported by the Hainan Natural Science Foundation under grant number 614249, and the Scientific Research Project of Hainan Education Department under grant number HNKY2014-111.


Haixia Long
email: haixia_long@163.com

Haiyan Fu

Chun Shi

Quantum-behaved particle swarm optimization based on Diversity-controlled

Keywords: global convergence, quantum-behaved particle swarm optimization, diversity-controlled, benchmark function

Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. But QPSO algorithm is to be easily trapped into local optima as a result of the rapid decline in diversity. So this paper describes diversity-controlled into QPSO (QPSO-DC) to enhance the diversity of particle swarm, and then improve the search ability of QPSO. The experiment results on benchmark functions show that QPSO-DC has stronger global search ability than QPSO and standard PSO.

Introduction

Particle swarm optimization (PSO) is a kind of stochastic optimization algorithms proposed by Kennedy and Eberhart [START_REF] Kennedy | Particle swarm optimization[END_REF] that can be easily implemented and is computationally inexpensive. The core of PSO is based on an analogy of the social behavior of flocks of birds when they search for food. PSO has been proved to be an efficient approach for many continuous global optimization problems. However, as demonstrated by F. Van Den Bergh [START_REF] Clerc | Discrete particle swarm optimization illustrated by the traveling salesman problem[END_REF], PSO is not a global convergence guaranteed algorithm because the particle is restricted to a finite sampling space for each of the iterations. This restriction weakens the global search ability of the algorithm and may lead to premature convergence in many cases. Several authors developed strategies to improve on PSO. Clerc [START_REF] Clerc | Particle swarm optimization[END_REF] suggested a PSO variant in which the velocity to the best point found by the swarm is replaced by the velocity to the current best point of the swarm, although he does not test this variant. Clerc [START_REF] Zhang | An adaptive PSO algorithm for reactive power optimization[END_REF] and Zhang et al. [START_REF] Eberhart | Comparing inertia weights and constriction factors in particle swarm optimization[END_REF] dynamically change the size of the swarm according to the performance of the algorithm. Eberhart and Shi [START_REF] He | A particle swarm optimizer with passive congregation[END_REF], He et al. [START_REF] Hu | Tracking dynamic systems with PSO: where's the cheese?[END_REF] adopted strategies based on dynamically modifying the value of the PSO parameter called inertia weight. Various other solutions have been proposed for preventing premature convergence: objective functions that change over time [START_REF] Parsopoulos | Particle swarm optimizer in noisy and continuously changing environments[END_REF]; noisy evaluation of the function objective [START_REF] Parsopoulos | On the computation of all global minimizers thorough particle swarm optimization[END_REF]; repulsion to keep particles away from the optimum [START_REF] Lozvbjerg | Extending particle swarms with self-organized criticality[END_REF]; dis-persion between particles that are too close to one another [START_REF] Blackwell | t push me! Collision-avoiding swarms[END_REF]; reduction of the attraction of the swarm center to prevent the particles clustering too tightly in one region of the search space [START_REF] Robinson | Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna[END_REF]; hybrids with other meta-heuristic such as genetic algorithms [START_REF] Hendtlass | A combined swarm differential evolution algorithm for optimization problems[END_REF]; or ant colony optimization [START_REF] Poli | Particle swarm optimization[END_REF]; an up-to-date overview of the PSO [START_REF] Sun | Particle swarm optimization with particles having quantum behavior[END_REF].

Recently, a new variant of PSO, called Quantum-behaved Particle Swarm Optimization (QPSO) [START_REF] Sun | A global search strategy of quantum behaved particle swarm optimization[END_REF][START_REF] Sun | Quantum-behaved particle swarm optimization with a hybrid probability distribution[END_REF], which is inspired by quantum mechanics and particle swarm optimization model. QPSO has only the position vector without velocity, so it is simpler than standard particle swarm optimization algorithm. Furthermore, several benchmark test functions show that QPSO performs better than standard particle swarm optimization algorithm. Although the QPSO algorithm is a promising algorithm for the optimization problems, like other evolutionary algorithm, QPSO also confronts the problem of premature convergence, and decrease the diversity in the latter period of the search. Therefore a lot of revised QPSO algorithms have been proposed since the QPSO had emerged. In Sun et al. [START_REF] Liu | Improving quantum-behaved particle swarm optimization by simulated annealing[END_REF], the mechanism of probability distribution was proposed to make the swarm more efficient in global search. Simulated Annealing is further adopted to effectively employ both the ability to jump out of the local minima in Simulated Annealing and the capability of searching the global optimum in QPSO algorithm [START_REF] Coelho | Novel Gaussian quantum-behaved particle swarm optimizer applied to electromagnetic design[END_REF]. Mutation operator with Gaussian probability distribution was introduced to enhance the performance of QPSO in Coelho [START_REF] Liu | Quantum-behaved particle swarm optimization with immune memory and vaccination[END_REF]. Immune operator based on the immune memory and vaccination was introduced into QPSO to increase the convergent speed by using the characteristic of the problem to guide the search process [START_REF] Angeline | Using Selection to Improve Particle Swarm Optimization[END_REF].

In this paper, QPSO with diversity-controlled is introduced. This strategy is to prevent the diversity of particle swarm declining in the search of later stage. The rest of the paper is organized as follows. In Section 2, the principle of the PSO is introduced. The concept of QPSO is presented in Section 3 and the QPSO with diversity-controlled is proposed in Section 4. Section 5 gives the numerical results on some benchmark functions and discussion. Some concluding remarks and future work are presented in the last section.

PSO Algorithm

In the original PSO with M individuals, each individual is treated as an infinitesimal particle in the D-dimensional space, with the position vector and velocity vector of particle i,

)) ( , ), ( ), ( ( ) ( 2 1 t X t X t X t X iD i i i   and )) ( , ), ( ), ( ( ) ( 2 1 t V t V t V t V iD i i i  
. The particle moves according to the following equations: is the position of the best particle among all the particles and is known as the global best position. The parameters 1 r and 2 r are two random num-bers distributed uniformly in (0, 1), that is

)) ( ) ( ( )) ( ) ( ( ) ( ) 1 ( 2 2 1 1 t X t P r c t X t P r c t V t V ij gj ij ij ij ij           (1) ) 1 ( ) ( ) 1 (     t V t X t X ij ij ij (2) for D j M i , 2 , 1 ; , 2 , 1     .
) 1 , 0 ( , 2 1 U r r . Generally, the value of V ij is restricted in the interval ] , [ max max V V 
. Many revised versions of PSO algorithm are proposed to improve the performance since its origin in 1995. Two most important improvements are the version with an Inertia Weight [START_REF] Shi | A Modified Particle Swarm[END_REF], and a Constriction Factor [START_REF] Clerc | The Particle Swarm: Explosion, Stability, and Convergence in a Multi-dimensional Complex Space[END_REF]. In the inertia-weighted PSO the velocity is updated by using

)) ( ( )) ( ) ( ( ) ( ) 1 ( 2 2 1 1 t X P r c t X t P r c t V w t V ij gj ij ij ij ij          
(3) while in the Constriction Factor model the velocity is calculated by using

))] ( ( )) ( ) ( ( ) ( [ ) 1 ( 2 2 2 1 t X P r c t X t P r c t V K t V ij gj ij ij ij ij            (4) where 4 , 4 2 2 2 1 2             c c k (5)
The inertia-weighted PSO was introduced by Shi and Eberhart [START_REF] He | A particle swarm optimizer with passive congregation[END_REF] and is known as the Standard PSO.

QPSO Algorithm

Trajectory analyses in Clerc and Kennedy [START_REF] Shi | Empirical Study of Particle Swarm Optimization[END_REF] demonstrated the fact that convergence of PSO algorithm may be achieved if each particle converges to its local attractor ) , , (

2 1 iD i i i p p p p   with coordinates ), ( )) ( ) ( ( ) ( 2 2 1 1 2 2 1 1 r c r c t P r c t P r c t p gj ij ij    or ) ( ) 1 ( ) ( ) ( t P t P t p gj ij ij        (6) where )
( 2 2 1 1 1 1 r c r c r c   
. It can be seen that the local attractor is a stochastic attractor of particle i that lies in a hyper-rectangle with i P and g P being two ends of its diagonal. We introduce the concepts of QPSO as follows.

Assume that each individual particle move in the search space with a  potential on each dimension, of which the center is the point ij p . For simplicity, we consider a particle in one-dimensional space, with point p the center of potential. Solving Schrödinger equation of one-dimensional  potential well, we can get the probability dis- tribution function

L x p e x D    2 ) (
. Using Monte Carlo method, we obtain

) 1 ln( 2 u L p x   ) 1 , 0 ( ~U u ( 7
)
The above is the fundamental iterative equation of QPSO.

In Sun et al. (2004b) a global point called Mainstream Thought or Mean Best Position of the population is introduced into PSO. The mean best position, denoted as C, is defined as the mean of the personal best positions among all particles. That is

              M i iD M i i M i i D t P M t P M t P M t C t C t C t C 1 1 2 1 1 2 1 ) ( 1 , , ) ( 1 , ) ( 1 )) ( , ), ( ), ( ( ) (   (8)
where M is the population size and i P is the personal best position of particle i. Then the value of L is evaluated by

) ( ) ( 2 t X t C L ij j    
and the position are updated by

) / 1 ln( ) ( ) ( ) ( ) 1 ( u t X t C t p t X ij j ij ij        (9)
where parameter  is called Contraction-Expansion (CE) Coefficient, which can be tuned to control the convergence speed of the algorithms. Generally, we always call the PSO with equation ( 9) Quantum-behaved Particle Swarm Optimization (QPSO), where parameter  must be set as 782 . 1   to guarantee convergence of the particle [START_REF] Sun | A global search strategy of quantum behaved particle swarm optimization[END_REF]. In most cases,  decrease linearly from 0

 to 1  ( 0  < 1  ).
We outline the procedure of the QPSO algorithm as follows: Procedure of the QPSO algorithm: Step1: Initialize the population;

Step2: Computer the personal position and global best position;

Step3: Computer the mean best position C;

Step4: Properly select the value of  ;

Step 5: Update the particle position according to Eq. ( 9); Step6: While the termination condition is not met, return to step (2); Step7: Output the results.

QPSO with diversity-controlled

QPSO is a promising optimization problem solver that outperforms PSO in many real application areas. First of all, the introduced exponential distribution of positions makes QPSO global convergent. The QPSO algorithm in the initial stage of search, as the particle swarm initialization, its diversity is relatively high. In the subsequent search process, due to the gradual convergence of the particle, the diversity of the population continues to decline. As the result, the ability of local search ability is continuously enhanced, and the global convergence ability is continuously weakened.

In early and middle search, reducing the diversity of particle swarm optimization for contraction efficiency improvement is necessary, however, to late stage of search, because the particles are gathered in a relatively small range, particles swarm diversity is very low, the global search ability becomes very weak, the ability for a large range of search has been very small, this algorithm will occur the phenomenon of premature.

To overcome this shortcoming, we introduce diversity-controlled into QPSO. The population diversity of the QPSO-DC is denoted as diversity (pbest) and is measured by average Euclidean distance from the particle's personal best position to the mean best position, namely [START_REF] Lozvbjerg | Extending particle swarms with self-organized criticality[END_REF] where M is the population of the particle, -A _is the length of longest the diagonal in the search pace, and D is the dimension of the problem. Hence, we may guide the search of the particles with the diversity measures when the algorithm is running.

In the QPSO-DC algorithm, only low bound d low is set for diversity (pbest) to prevent the diversity from constantly decreasing. The procedure of the algorithm is as follows.

After initialization, the algorithm is running in convergence mode. In process of convergence, the convergence mode is realized by Contraction-Expansion (CE) Coeffi-cient. On the course of evolution, if the diversity measure diversity (pbest) of the swarm drops to below the low bound d low , the mean best position is reinitialized.

Experiment Results and Discussion

To test the performance of the QPSO with diversity-controlled, seven widely known benchmark functions listed in V for SPSO. The fitness value is set as function value and the neighborhood of a particle is the whole population.

As in Angeline [START_REF] Shi | A Modified Particle Swarm[END_REF], for each function, three different dimension sizes are tested. They are dimension sizes: 10, 20 and 30. The maximum number of generations is set as 1000, 1500, and 2000 corresponding to the dimensions 10, 20, and 30 for first six functions, respectively. The maximum generation for the last function is 2000.In order to investigate whether the QPSO-RS algorithm well or not, different population sizes are used for each function with different dimension. They are population sizes of 20, 40, and 80.For SPSO, the acceleration coefficients are set to be c1=c2=2 and the inertia weight is decreasing linearly from 0.9 to 0.4 as in Shi and Eberhart [25]. In experiments for QPSO, the value of CE Coefficient varies from 1.0 to 0.5 linearly over the running of the algorithm as in [START_REF] Liu | Improving quantum-behaved particle swarm optimization by simulated annealing[END_REF], while in QPSO-DC, the value of CE Coefficient is listed in Table 3 andTable 4. From the Table 3 and Table 4, we also obtain the CE COFFICIENT of QPSO-DC decreases from 0.8 to 0.5 linearly. We had 50 trial runs for every instance and recorded mean best fitness and standard deviation

The mean values and standard deviations of best fitness values for 50 runs of each function are recorded in Table 5 to Table 11.

Table 1. Expression of the five tested benchmark functions Function Expression

Search Domain The results show that both QPSO, QPSO-DC are superior to SPSO except on Schwefel and Shaffer's f6 function. On Shpere Function the QPSO works better than QPSO-DC when the warm size is 40 and dimension is 10, and when the warm size is 80 and dimension is 20. Expect for the above two instance, the best result is QPSO-DC. The Rosenbrock function is a mono-modal function, but its optimal solution lies in a narrow area that the particles are always apt to escape. The experiment results on Rosenbrock function show that the QPSO-DC outperforms the QPSO. Rastrigrin function and Griewank function are both multi-modal and usually tested for comparing the global search ability of the algorithm. On Rastrigrin function, it is also shown that the QPSO-DC generated best results than QPSO. On Griewank function, QPSO-DC has better performance than QPSO algorithm. On Ackley function, QPSO-DC has best performance when the dimension is 10, except for above functions, the QPSO-DC has minimal value. On the Schwefel function, SPSO is best performance in any situation. On Shaffer's function, it has sub-optima 0.0097. The QPSO-DC shows its stronger ability to escape the local minima 0.0097 than the QPSO, and even than SPSO. Generally speaking, the QPSO-DC has better global search ability than SPSO and QPSO.

Sphere    n i i x X f 1 2 ) ( 1 100 100    i x Rosen- brock          1 1 ) 2 ) 1 ( 2 ) 2 1 ( 100 ( ) ( 2
n i i x i x i x X f 100 100    i x Rastrigrin       n i i x i x X f 1 ) 10 ) 2 cos( 10 2 ( ) ( 3  10 10    i x Greiwank                n i n i i i x i x X f 1 1 1 cos 2 4000 1 ) ( 4 600 600    i x Ackley           n i i x n e n i i x n e e f
Figure 1 shows the convergence process of the three algorithms on the first four benchmark functions with dimension 30 and swarm size 40 averaged on 50 trail runs. It is shown that, although QPSO-DC converge more slowly than the QPSO during the early stage of search, it may catch up with QPSO at later stage and could generate better solutions at the end of search.

Figure1.

  Figure1. Convergence process of the three algorithm on the first four benchmark functions with dimension 30 and swarm size 40 averaged on 50 trail runsFrom the results above in the tables and figures, it can be concluded that the QPSO-DC has better global search ability than SPSO, QPSO.

  The parameters 1

											c and 2 c are called the acceleration co-
	efficients. Vector	i P		( P 1 i	,	2 P i	,  ,	iD P	)	known as the personal best position, is the best
	previous position (the position giving the best fitness value so far) of particle i, vector
	g P		, 1 g P (	g P	2	,  ,	) gD P

  Table 1 are tested for comparison with Standard PSO (SPSO), QPSO. These functions are all minimization problems with minimum objective function values zeros. The initial range of the population listed in Table 2 is asymmetry as used in Shi and Eberhart [25]. Table 2 also lists max

Table 2 . The initial range of population for all the tested algorithms and Vmax for SPSO Initial Range Vmax

 2

	5	20			20		5 1	1		1	2					1	1	cos(	2		)		30		i x		30
	Schwefel	f	6		418	.	9829	n		i	n  	1	(	x	i	sin	x	i	)					500		i x		500

Table 3 . Parameter value of QPSO-dc

 3

	CE	Sphere function	Rosenbrock func-tion	Rastrigrin function
	Coefficient	fitness	St. Dev.	fit-ness	St. Dev.	fitness	St. Dev.
	(1.0,0.5)	1.0050e-013	2.8029e-013	63.6787	112.2467	96.4876	11.2102
	(0.9,0.5)	2.1644e-017	3.3232e-017	53.2366	75.9345	94.8874	11.2350
	(0.8,0.5)	4.9846e-026	1.7413e-025	39.2303	37.6453	89.7231	9.9548
	(0.7,0.5)	9.7934e-022	3.6896e-021	73.4979	100.0602	83.4991	11.6756
	(1.0,0.4)	4.6673e-012	2.4765e-011	48.4907	46.6895	81.5502	13.5262
	(0.9,0.4)	1.9889e-014	7.9829e-014	78.9340	124.4234	80.8902	12.1062
	(0.8,0.4)	6.0362e-015	3.4761e-014	72.1817	102.6235	74.1639	10.4042
	(0.7,0.4)	1.6104e-018	4.5714e-018	102.6623	155.5243	70.7840	15.0399
	(1.0,0.3)	1.7248e-010	4.4884e-010	59.0231	74.3503	64.9413	19.3783
	(0.9,0.3)	1.0957e-011	6.1253e-011	76.6616	103.9478	59.4859	17.9470
	(0.8,0.3)	6.2854e-014	1.2280e-013	80.0582	91.4265	56.3751	17.4575
	(0.7,0.3)	2.4054e-013	9.3876e-013	106.6513	133.7599	48.8128	20.5664

Table 4 . Table 3 (continue)

 4

	CE Coeffi-cient	Griewank func-tion fitness St. Dev.	Ackley function fit-ness St. Dev.	Schwefel function fitness Dev. St.	Shaffer's f6 function fit-St. ness Dev.
	(1.0,0.5)	0.0219	0.0254	3.0209e-013	4.3354e-013	5.0840e+003 238.6654	7.7802e-004	0.0027
	(0.9,0.5)	0.0165	0.0171	3.2649e-014	8.3201e-014	4.9791e+003 218.1045	5.8366e-004	0.0023
	(0.8,0.5)	0.0078	0.0096	1.0978e-014	9.2924e-015	3.9491e+003 682.3425	7.8734e-004	0.0027
	(0.7,0.5)	0.0141	0.0183	3.9826e-014	5.6935e-014	4.4273e+003 347.6864	0.0019	0.0039
	(1.0,0.4)	0.0131	0.0170	3.7380e-011	1.3192e-010	5.0548e+003 251.4751	5.8369e-004	0.0023
	(0.9,0.4)	0.0182	0.0191 1.5916e-3.8720e-5.0319e+003 238.8422 7.9738e-	0.0027

Table 5 . Numerical results on Sphere function

 5

	M	Dim.	Gmax	fitness	SPSO St. Dev.	fitness	QPSO St. Dev.	QPSO-DC fitness St. Dev.
		10	1000 4.6119e-021 1.0352e-020 3.1979e-043 2.2057e-042 5.2719e-045 1.5684e-045
	20	20	1500 9.0266e-012 3.5473e-011 1.9197e-024 7.1551e-024 2.5427e-026 4.2876e-026
		30	2000 3.9672e-008 7.0434e-008 7.3736e-015 2.1796e-014 3.2486e-016 5.3971e-016
		10	1000 1.3178e-024 3.7737e-024 2.7600e-076 1.8954e-075 8.4527e-074 2.3460e-074
	40	20	1500 2.3057e-015 6.6821e-015 3.9152e-044 1.8438e-043 5.4381e-045 8.5426e-045
		30	2000 1.1286e-010 2.2994e-010 2.6424e-031 6.3855e-031 2.5419e-032 1.3645e-032
		10	1000 1.6097e-028 7.1089e-028 2.5607e-103 6.4847e-103 1.9824e-104 2.5681e-104
	80	20	1500 6.3876e-018 1.4821e-017 1.4113e-068 7.4451e-068 1.5873e-068 4.3876e-068
		30	2000 3.2771e-013 7.5971e-013 6.5764e-050 3.6048e-049 2.0254e-050 1.2574e-050

Table 6 . Numerical results on Rosenbrock function

 6

	M	Dim.	Gmax	fitness	SPSO St. Dev.	QPSO fitness St. Dev.	QPSO-DC fitness St. Dev.
		10	1000	51.0633	153.7913	9.5657	16.6365	7.5341	0.8634
	20	20	1500	100.2386	140.9822	82.4294	138.2429	32.8419	28.3476
		30	2000	160.4400	214.0316	98.7948	122.5744	69.3102	54.3791
		10	1000	24.9641	49.5707	8.9983	17.8202	4.9810	3.2094
	40	20	1500	59.8256	95.9586	40.7449	41.1751	15.3429	12.3076
		30	2000	124.1786	269.7275	43.5582	38.0533	35.2487	24.8619
		10	1000	19.0259	41.6069	6.8312	0.3355	4.1086	3.5476
	80	20	1500	40.2289	46.8491	33.5287	31.6415	15.6271	0.0367
		30	2000	56.8773	57.8794	44.5946	31.6739	22.3706	0.1648

Table 7 . Numerical results on Rastrigrin function

 7

					SPSO	QPSO	QPSO-DC
	M	Dim.	Gmax	fitness	St. Dev.	fitness	St. Dev.	fitness	St. Dev.
		10	1000	5.8310	2.5023	4.0032	2.1409	2.6871	1.9684
	20	20	1500	23.3922	6.9939	15.0648	6.0725	13.5970	12.3458
		30	2000	51.1831	12.5231	28.3027	12.5612	22.3694	4.2061
		10	1000	3.7812	1.4767	2.6452	1.5397	1.8541	2.1380
	40	20	1500	18.5002	5.5980	11.3109	3.5995	12.3471	7.2684
		30	2000	39.5259	10.3430	18.9279	4.8342	14.3796	6.9173
		10	1000	2.3890	1.1020	2.2617	1.4811	1.0367	1.3574

Table 8 . Numerical results on Griewank function

 8

					SPSO	QPSO	QPSO-DC
	M	Dim.	Gmax	fitness	St. Dev.	fitness	St. Dev.	fitness	St. Dev.
		10	1000	0.0920	0.0469	0.0739	0.0559	0.0657	0.0502
	20	20	1500	0.0288	0.0285	0.0190	0.0208	0.0262	0.0227
		30	2000	0.0150	0.0145	0.0075	0.0114	0.0082	0.0235
		10	1000	0.0873	0.0430	0.0487	0.0241	0.0587	0.0822
	40	20	1500	0.0353	0.0300	0.0206	0.0197	0.0135	0.0431
		30	2000	0.0116	0.0186	0.0079	0.0092	0.0020	0.0123
		10	1000	0.0658	0.0266	0.0416	0.0323	0.0424	0.0682
	80	20	1500	0.0304	0.0248	0.0137	0.0135	0.0050	0.0103
		30	2000	0.0161	0.0174	0.0071	0.0109 1.0570e-006 5.3089e-006

Table 9 . Numerical results on Ackley function

 9

					SPSO	QPSO	QPSO-DC
	M	Dim.	Gmax	fitness	St. Dev.	fitness	St. Dev.	fitness	St. Dev.
		10	1000 2.0489e-011 3.0775e-011 6.8985e-012	1.2600e-011	2.7356e-015 5.0243e-016
	20	20	1500	0.0285	0.2013	1.5270e-008	2.1060e-008	9.7700e-015 5.8751e-015
		30	2000	0.2044	0.4899	4.3113e-007	4.4188e-007	7.1797e-013 1.5299e-012
		10	1000 2.4460e-013 5.2901e-013 2.5935e-015	5.0243e-016	2.6645e-015	0
	40	20	1500 2.6078e-008 4.0653e-008 6.3491e-013	1.3305e-012	3.5882e-015 1.5742e-015
		30	2000 3.7506e-006 6.4828e-006 5.5577e-011	8.6059e-011	6.5015e-015 1.8772e-015
		10	1000 5.5778e-015 7.4138e-015 2.4514e-015	8.5229e-016	2.2382e-015 1.1662e-015
	80	20	1500 5.2979e-010 7.2683e-010 5.8620e-015	1.6446e-015	2.7356e-015 5.0243e-016
		30	2000 1.6353e-007 2.1300e-007 6.7253e-014	4.4759e-014	5.4357e-015 1.4866e-015

Table 10 . Numerical results on Schwefel function

 10

	M	Dim.	Gmax	SPSO fitness St. Dev.	fitness	QPSO St. Dev.	QPSO-DC fitness St. Dev.
		10	1000	630.6798 169.9164 1.0784e+003 342.1967	740.4763	293.0045
	20	20	1500 2.0827e+003 327.0313 4.0026e+003 679.1461 3.4182e+003 1.0382e+003
		30	2000 4.1097e+003 481.8801 6.5075e+003 589.9931 6.8196e+003 1.1614e+003
		10	1000	494.0236 169.5594 1.4359e+003 246.9290	876.2897	382.1407
	40	20	1500 1.8516e+003 273.8690 4.5580e+003 242.8220 3.9461e+003 816.6767
		30	2000 3.4042e+003 384.3150 7.3380e+003 288.9463 7.3466e+003 879.8090
		10	1000	426.0796 130.5357 1.6060e+003 132.0009	979.6775	363.0876

Table 11 . Numerical results on Shaffer's f6 function

 11

	M	Dim.	Gmax	fitness	SPSO St. Dev.	fitness	QPSO St. Dev.	QPSO-DC fitness St. Dev.
	20	2	2000 3.8965e-004 0.0019	0.0016		0.0036	1.3764e-007 7.3542e-007
	40	2	2000 1.9432e-004 0.0014	5.8308e-004	0.0023	1.2681e-008 3.6984e-008
	80	2	2000	0.0000	0.0000	7.5695e-009 2.5080e-008 5.9540e-009 8.0367e-009


Ying Luo

Haiyan Fu

Modified K-means Algorithm for Clustering Analysis of Hainan Green Tangerine Peel

Keywords: clustering, Modified K-means algorithm, initial clustering center, average link

K-means is a classic, the division of the clustering algorithm, apply to the classification of the globular data. According to the initial clustering center, this paper comprehensive consideration the characteristics of various Hierarchical cluster algorithms and choose the appropriate Hierarchical cluster algorithm to improve K-means, and combined with Hainan Green Tangerine Peel cluster analysis of data which is compared experiments. The results indicate that the improved algorithm have increasing the distance between classes with each others, get a stable of cluster results and better implementation data mining. Finally to summary the two algorithms and the further research direction.

Introduction

The K-means is a classic clustering algorithm which is a data exploration technique that allows samples with similar characteristics to be clustered together in order to facilitate their further processing and usually has applications in identification of patterns hidden in data. Although this algorithm has high flexibility and efficiency, in some case, it has not a good performance for random initialization centre. Owing to trial and error to achieve good representation it spend more memory and CPU time.

To address this issue, some scholar suggested improving algorithm based on other clustering algorithm that has better performance with speed, efficiency and clustering results than previous traditional clustering algorithms when solve some specified problems.

In this paper, a modified K-means algorithm based on two stages strategy is introduced. Modified K-means employed a hierarchical clustering algorithm-average linkage technology-to initialize the centre for the second stage and then uses the traditional K-means for further classification.

Related Research

In the traditional K-means, process depends on a near-optimal solution. For the first iteration, the algorithm is initialized randomly. Then, the clustering centers from the last iteration are used to initialize in subsequent iterations till there is no change in the average value. In worse case, a group unavailable initial clustering center lead to the K-means stops to a local minimum, that it to say, their true distribution in the problem domain will not be reflected by the distribution of objects in the result [START_REF] Jin | A k-means algorithm based on Hierarchical clustering[END_REF].

Hierarchy clustering is famous because of easy to read, but it not flexible enough due to cannot be operated with samples reversibly. Initially, all genes are considered as individual clusters followed by sequential merging of the two closest clusters in each subsequent step based on their distance, the final step has only one clustering left with all the genes in it [START_REF] Lin | Data mining and OLAP theory and practice[END_REF]. So, this paper argues that, hierarchical clustering is suitable for used to preliminary classification. In lots of representative algorithm, there are three different technology; single linkage, average linkage and complete linkage.

The single linkage technology uses the distance of samples with most similar in different groups. Sometimes, a clustering distribution is non-homogeneous that has a bad impact on the performance of the algorithm. The complete linkage technology, also called furthest neighbor, uses the distance of samples with most dissimilar in two groups and as a complete vision, they have a better performance. However, this technology is susceptible to noise, and unable to get satisfactory results due to few samples which away from the center of group. The third technology is the average linkage technology that uses the average distance between all pairs in different clustering. This algorithm considers the structure of dataset, the most similarity group tend to be merged.

1 1 1 ( , ) ( , ) s r n n ri sj i j r s d r s dist x x n n     (1)
where r and s are group, there are r n samples in group r, and s n samples in group s. Therefore, employed average linkage technology in subset of dataset for achieve initial clustering center to the second stage is available.

Algorithm Design

The modified K-means algorithm works in two stages: The first stage makes preliminary classification with the average linkage technology. The second stage uses the clustering centers from the first stage as initial clustering center and employed the traditional K-means algorithm on the further classification. K is the expected number of clusters, in practice, it is empirically chosen by the user depending on characteristics of the dataset [START_REF] Hu | A Hybrid Clustering Algorithm and It's Application[END_REF]. Algorithm process is as follows: The first stage:

Step 1: Choose appropriate function calculate distance metric used for similarity of samples. The details of function choosing are explained in later.

Step 2: The similarity samples x i and x j are merged, the first clustering named D1, D1= {x i , x j }.

Step 3: Another sample x p is added to the D1 if the average distance between x p and samples that in the D1 less than Threshold, and then D1= {x i , x j , x p }.Else, x p is taken out and a new clustering is made with it.

Step 4: Step 3 are repeated till the number of cluster is equal to k.

The second stage:

Step 1: clustering centers from the first stage are initialized.

Step 2: Samples in dataset are assigned to the cluster that the most similar initial centers are.

Step 3: Recalculated clustering average value as centers for the sequential iterative.

Step 4: Step 2 and Step 3 are repeated till there is no change in clusters. In the first stage, some function are common used for computes the distance metric that defined dissimilarity between samples, such as Euclidean, Hamming distance and so on. The Hamming distance between two samples is the percentage of coordinates that differ.

  1 [ ] [ ] ( , ) m i j i j m x v x v d x x m          (2)
where x i and x j are two samples in sets

{ | 1,2, } j X x j n    ,  
v  is the  -th attribute, and each samples have m attributes and they are discrete.

Clustering performance criterion function

Clustering is aimed to divide a set of samples

1 2 { , , , } n X x x x  
into K disjointed subsets X 1 ; X 2 ; …; X K so that points in the same subset share common properties while points which belong to different subsets do not share these properties. We evaluate the clustering performance using error sum of squares criterion function, it is defined as:

1 i K i i p X ESS p m       (3)
where average vector

1 1, 2, , i i p X i m p i k n      .
Algorithm steps are as follows: input:

1 2 { , , , } n X x x x  
samples sets K numbers of clustering output:

1 2 { , , } K S X X X  
the results of clustering the dissimilarity function: hamming distance criterion function: error sum of squares criterion function ESS initial state:

0 d  %initial threshold 1 2 {{ },{ }, { }} n S x x x  
D if average d  then 1 { , , } i j p D x x x  else 2 { } p D x  1 d d  
until the number of clustering is equal to K. The first stage is important to the whole process of avoid some worse case which dependent on random initialization of the traditional K-means. While the average linkage technology is completed, lots of samples are in true distribution in the problem domain and reduce the number of iterative in the second stage.

Two parameters are compared during the testing: clustering quality and stability.

Comparison for Green Peel Cluster Analysis of Hainan Island

The performance of the modified K-means algorithm is compared with traditional Kmeans algorithms using our dataset that comes from the experiment of Green Tangerine Peel clustering Analysis of Hainan. We select 100 leaves evenly with DNA mo-lecular markers method at five regions (group) in Hainan Island: ShiMeiWan (SMW), SanYa (SY), BaWangLing (BWL), JianFengLing (JFL), and WenChang (WC).

The dataset has 100 arrays, 206 columns (each sample has 206 characteristics) and 15 times are run for each test. All data as follow are the average value.

Two algorithms are run with Inter(R) Core(TM) 2Duo CPU T6500@2.1GHz. and 2.0GB RAM. The operation system is Vista32 and the software is written in MATLAB7.0.

The centroid of the cluster in the second stage of modified K-means is obtained by average linkage technology, and traditional K-means randomly chooses K points to be the initial centroids, we use hamming distance to calculate the dissimilarity.

Evaluation of clustering quality

The goal of clustering is to minimize the intra-cluster distances and to maximize the inter-cluster distances [START_REF] Hu | A Hybrid Clustering Algorithm and It's Application[END_REF]. In order to evaluate the quality of the results of the two algorithms, the distance between centers in two clusters are used to intra-cluster distances while distances between a clustering center and the objects belonging to it are inter-cluster distances. We clustered dataset and get the results of 2-10 numbers clustering. On the whole, the performance of modified K-means are superior to the traditional K-means algorithm (the average inter-cluster distances are smaller).

Stability of the clustering

For compare the stability which is important that reflects the similarity between different runs using the same program of each algorithm, we need a statistic. In the Kmeans algorithm, based on iterative till there is no change in each cluster, at the same time, the value of the total amount of distortion wills no change more. The distortion of a cluster which describes the dispersion degree in the distribution of the samples is defined as the sum of the squared Hamming distances between its centre and the objects belonging to it.

 

2 1 i ESS ESS S m     (4)
Standard deviation of distortion in test using the same program could distribution the range of distortion. If vary of standard deviation of distortion are wildly, the clustering results are unstable, and vice versa. For comparison, we have run 12 times for each and recorded the result. The table 2 shows, as we can see, the first stage of the modified K-means benefited from the average link technique performs (the standard deviation of distortion =0) better than the corresponding (which is>0). Namely, the stability of cluster is improved.

Significantly, the modified K-means clustering algorithm is better than the traditional K-means in time taken, clustering quality, and stability of algorithm.

Summary

The modified algorithm employed a hierarchical clustering algorithm-average linkage technology-to initialize the centre avoid a bad clustering results in finally by samples which are in the edge of the group as center in the first iterative in the traditional K-means algorithm. At the same time, thanks to reducing the influence by noise, the modified algorithm having improve the Evaluation of clustering quality in a certain degree. Besides, random selection of the initial clustering center lead to the algo-rithm stops in a local minimum and achieve different results in many tests and modified K-means is an available strategy in this solution. However, as the K-means, the modified K-means control the border of clustering with diameter of semi-sphere, if the clustering is not spherical, two algorithms will not work very well.

Aknowledgements.

This work is supported by NSF of China (70940007 and 71461008).

5

 5 step :update centriod, recalculated clustering average value as centriod for the sequential iterative. 6 step : 5 step are repeated until the criterion function -error sum of squares criterion function is constriction.

Figure 1 .

 1 Figure 1. the "o" is on behalf the average intercluster distances of the K-means algorithm, and the "*" is represent the corresponding of the modified K-means.

Table 1 .

 1 the intra-cluster distances of cluster used in two algorithms

	cluster	2		3		4		5		6		7	8	9	10
	K-	0.500	0.599	0.621		0.565		0.606		0.624	0.635	0.635	0.633
	means
	AK-	0.602	0.603	0.605		0.593		0.601		0.609	0.615	0.620	0.633
	means
				0.45
				0.4
			Average Intercluster Distances	0.15 0.2 0.25 0.3 0.35
				0.1
				0.05
				0	0	10	20	30	40	50	60	70	80	90	100
										K

Table 2 .

 2 Comparison of standard deviation of distortion to complete analysis of two clustering algorithms

	cluster	2	3	4	5	6	7	8	9	10
	K-means	2.499	1.929	2.334	1.606	1.255	0.654	0.358	0.178	0
	AK-means	0	0	0	0	0	0	0	0	0