Immunology as a Metaphor for Computational ... - Napier University

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Chapter 7. Conclusion 189Clustering and a Kohonen Network on Fisher’s Iris data set. He concludes that “theAIS provides a more diverse representation of the data than the other two methodswhich is useful for classifying unseen data and variations on unseen data, and thatin addition, the AIS is effective at allowing the user to explore the evolved network togain a fuller understanding of the makeup of the dataset, thus aiding the data-miningand analysis process.”. Therefore, it is necessary to consider whether either a selforganisingmap approach, as typified by the Kohonen network, could have been usedto cluster the artificial data-sets described in chapter 4 and tested with the COSDM andSOSDM models.The Kohonen network can clearly be applied to cluster static data sets, although[Timmis et al., 1999] found that it was unable to discover the three distinct classespresent in the Fisher data set, whereas his AIS algorithm was able to correctly identifythree separate clusters. However, due to the arguments outlined in chapter 2, section2.3, it is unsurprising that the Kohonen algorithm could not correctly cluster this data— an unsupervised clustering algorithm could not correctly classify this data due tothe geometric intermingling of the data classes. However, despite this, it is clear thatTimmis’s AINE algorithm does visually produce three distinct clusters whereas theKohonen algorithm does not, therefore it is of benefit in discovering general featuresin data-sets, even if the classification accuracy is not 100%.The Kohonen algorithm in its original form is not suitable for clusteringnon-stationary data, although extensions to it have been suggested, for example[Abrantes and Marques, 1998] give a framework for clustering dynamic image andvideo data based on the Kohonen algorithm. Other algorithms for clustering dynamicdata also exist; Fritzke’s Growing Neural Gas algorithm was described in chapter6, and as mentioned, has been applied to cluster slowly changing distributions,[Fritzke, 1997a]. It would be interesting to examine how this algorithm performs onthe data used in this thesis — this work will be performed in the future. A point to notehowever is that clustering algorithms such as GNG and Kohonen rely on calculatingthe Euclidean distance between vectors in order to cluster them — the data-sets describedin chapter 4 consist of binary data, and hence it might be more appropriate tomodify these algorithms to use Hamming distance as a measure of similarity between
Chapter 7. Conclusion 190vectors.However, the SOSDM based on a combination of ideas from the immune-systemand Sparse Distributed Memories may offer some advantages over the standard clusteringalgorithms. The most obvious advantage is that SOSDM is an associative memory,as well as a means of clustering data. Thus, data can be stored and retrieved fromthe memory if necessary, and new data easily be categorised on presentation to thememory. An important feature of the SOSDM not provided by the other clusteringtechniques is that the combination of the address and counters defining the centre ofeach hard location in the SOSDM provides a concise description of the cluster itself;this information is not available from either the GNG or Kohonen algorithms. Themethod also provides a mechanism for identifying anomalous data, either by monitoringthe system for appearance of new hard locations and hence clusters, or trackinglarge movements in existing clusters. A further point to consider is also that theSOSDM algorithm allows data to belong to more than one cluster — in real databases,clusters will rarely be able to be completely isolated, therefore this feature may beuseful.Finally, [Dasgupta, 1997] has made clear the relationship between immune systemsand neural networks. A neural network is not a suitable tool for clustering data,though could be used to perform anomaly detection in data by training the networkwith examples of items from known clusters. This obviously requires previous classificationof the data into clusters. However, the idea is mentioned here as a recurrentnetwork could provide a means of performing anomaly detection in a non-stationaryenvironment with continuously adapting weights.In summary, the models presented in this thesis do suggest that the immunemetaphor might provide features not apparent in other more established techniquesfor tackling both scheduling problems and dynamic data-clustering problems. It seemsclear that the way forward is not to attempt to adhere strictly to biological principleswhen building artificial models, but to use them as inspiration for suitable computationaltechniques, modifying principles and adding new ones where necessary. This isnot a drawback — many successful neural network applications are built on the backof the back-propagation algorithm, yet it is clear that this algorithm is not utilised by
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AbstractThe biological immune syste
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DeclarationI declare that this thes
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Table of Contents1 Introduction 11.
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5.3.1 Default Parameters . . . . .
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List of Figures2.1 ’Lock and Key
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List of Tables2.1 Structural and fu
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Chapter 1IntroductionThe study of b
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Chapter 4Applying an Immune System
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Chapter 5. EA Based Model — COSDM
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Page 220 and 221: Bibliography 205[Farmer et al., 198
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Page 226 and 227: Bibliography 211[Potter and De Jong
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