Using sequential deviation to dynamically determine the number of clusters found by a local network neighbourhood artificial immune system

Abstract: Many of the existing network theory based artificial immune systems have been applied to data clustering. The formation of artificial lymphocyte (ALC) networks represents potential clusters in the data. Although these models do not require any user specified parameter of the number of required clusters to cluster the data, these models do have a drawback in the techniques used to determine the number of ALC networks. This paper discusses the drawbacks of these techniques and proposes two alternative techniques which can be used with the local network neighbourhood artificial immune system. The end result is an enhanced model that can dynamically determine the number of clusters in a data set.

[1]  Prabhakar Raghavan,et al.  A Linear Method for Deviation Detection in Large Databases , 1996, KDD.

[2]  K. Huang,et al.  A synergistic automatic clustering technique (SYNERACT) for multispectral image Analysis , 2002 .

[3]  Mark James Neal,et al.  Meta-stable Memory in an Artificial Immune Network , 2003, ICARIS.

[4]  Fernando José Von Zuben,et al.  An Evolutionary Immune Network for Data Clustering , 2000, SBRN.

[5]  H. Abbass,et al.  aiNet : An Artificial Immune Network for Data Analysis , 2022 .

[6]  Leandro Nunes de Castro,et al.  aiNet: An Artificial Immune Network for Data Analysis , 2002 .

[7]  David L. Dowe,et al.  Intrinsic classification by MML - the Snob program , 1994 .

[8]  Siddheswar Ray,et al.  Determination of Number of Clusters in K-Means Clustering and Application in Colour Image Segmentation , 2000 .

[9]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[10]  Michalis Vazirgiannis,et al.  Cluster validity methods: part I , 2002, SGMD.

[11]  María Cristina Riff,et al.  Towards an immune system that solves CSP , 2007, 2007 IEEE Congress on Evolutionary Computation.

[12]  Greg Hamerly,et al.  Learning the k in k-means , 2003, NIPS.

[13]  Julius T. Tou,et al.  Dynoc—A dynamic optimal cluster-seeking technique , 1979, International Journal of Computer & Information Sciences.

[14]  Erik K. Antonsson,et al.  Dynamic partitional clustering using evolution strategies , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[15]  N K Jerne,et al.  Towards a network theory of the immune system. , 1973, Annales d'immunologie.

[16]  Jerne Nk.  Towards a network theory of the immune system. , 1974 .

[17]  E. Forgy,et al.  Cluster analysis of multivariate data : efficiency versus interpretability of classifications , 1965 .

[18]  Rajagopalan Srinivasan,et al.  An Information Theory Approach for Validating Clusters in Microarray Data , 2004 .

[19]  Olfa Nasraoui,et al.  Mining Evolving User Profiles in Noisy Web Clickstream Data with a Scalable Immune System Clustering Algorithm , 2003 .

[20]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[22]  G H Ball,et al.  A clustering technique for summarizing multivariate data. , 1967, Behavioral science.

[23]  Andries Petrus Engelbrecht,et al.  Towards a self regulating local network neighbourhood artificial immune system for data clustering , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[24]  Geoffrey I. Webb,et al.  Proceedings of the 17th Australian Joint Conference on Artificial Intelligence , 2004 .

[25]  Cor J. Veenman,et al.  A Maximum Variance Cluster Algorithm , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Jonathan Timmis,et al.  A resource limited artificial immune system for data analysis , 2001, Knowl. Based Syst..

[27]  Csaba Legány,et al.  Cluster validity measurement techniques , 2006 .

[28]  L. Wasserman,et al.  A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion , 1995 .

[29]  Andries Petrus Engelbrecht,et al.  A local network neighbourhood artificial immune system for data clustering , 2007, 2007 IEEE Congress on Evolutionary Computation.

[30]  Christophe Rosenberger,et al.  Unsupervised clustering method with optimal estimation of the number of clusters: application to image segmentation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[31]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[32]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[33]  Michalis Vazirgiannis,et al.  Clustering validity checking methods: part II , 2002, SGMD.