Integration of artificial immune network and K-means for cluster analysis

This study is dedicated to propose a cluster analysis algorithm which is integration of artificial immune network (aiNet) and K-means algorithm (aiNetK). Four benchmark data sets, Iris, Wine, Glass, and Breast Cancer, are employed to testify the proposed algorithm. The computational results reveal that aiNetK is superior to particle swam optimization and artificial immune system-related methods.

[1]  C BezdekJames A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms , 1980 .

[2]  Furong Liu,et al.  Survey of artificial immune system , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[3]  Olfa Nasraoui,et al.  A framework for mining evolving trends in Web data streams using dynamic learning and retrospective validation , 2006, Comput. Networks.

[4]  R. J. Kuo,et al.  An application of particle swarm optimization algorithm to clustering analysis , 2011, Soft Comput..

[5]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[6]  Nong Sang,et al.  A study on semi-supervised FCM algorithm , 2012, Knowledge and Information Systems.

[7]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[8]  Hai Jin,et al.  Energy optimization schemes in cluster with virtual machines , 2010, Cluster Computing.

[9]  Chui-Yu Chiu,et al.  Applying artificial immune system and ant algorithm in air-conditioner market segmentation , 2009, Expert Syst. Appl..

[10]  Genichi Taguchi,et al.  Taguchi's Quality Engineering Handbook , 2004 .

[11]  Hualong Xu,et al.  One Immune Simplex Particle Swarm Optimization and It's Application , 2008, 2008 Fourth International Conference on Natural Computation.

[12]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[13]  R. J. Kuo,et al.  Application of ant K-means on clustering analysis , 2005 .

[14]  Maoguo Gong,et al.  Immunodominance and clonal selection inspired multiobjective clustering , 2009 .

[15]  Ickjai Lee,et al.  AMOEBA: HIERARCHICAL CLUSTERING BASED ON SPATIAL PROXIMITY USING DELAUNATY DIAGRAM , 2000 .

[16]  Amir B. Geva,et al.  Hierarchical unsupervised fuzzy clustering , 1999, IEEE Trans. Fuzzy Syst..

[17]  James C. Bezdek,et al.  A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  V. Rao Vemuri,et al.  An artificial immune system approach to document clustering , 2005, SAC '05.

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

[20]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[21]  Qing He,et al.  Effective semi-supervised document clustering via active learning with instance-level constraints , 2011, Knowledge and Information Systems.

[22]  Yang Yan,et al.  Semi-supervised fuzzy co-clustering algorithm for document categorization , 2011, Knowledge and Information Systems.

[23]  Reda Younsi,et al.  A New Artificial Immune System Algorithm for Clustering , 2004, IDEAL.

[24]  James C. Bezdek,et al.  Numerical convergence and interpretation of the fuzzy c-shells clustering algorithm , 1992, IEEE Trans. Neural Networks.

[25]  Maria Virvou,et al.  Artificial Immune System-Based Customer Data Clustering in an e-Shopping Application , 2006, KES.

[26]  Fernando José Von Zuben,et al.  Adaptive Radius Immune Algorithm for Data Clustering , 2005, ICARIS.

[27]  Bin Lu,et al.  An optimized genetic K-means clustering algorithm , 2012, 2012 International Conference on Computer Science and Information Processing (CSIP).

[28]  James C. Bezdek,et al.  Clustering with a genetically optimized approach , 1999, IEEE Trans. Evol. Comput..

[29]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[30]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[31]  Peter Ross,et al.  Exploiting the Analogy between the Immune System and Sparse Distributed Memories , 2003, Genetic Programming and Evolvable Machines.

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

[33]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[34]  Ioannis A. Maraziotis,et al.  A semi-supervised fuzzy clustering algorithm applied to gene expression data , 2012, Pattern Recognit..

[35]  Khaled S. Al-Sultan,et al.  A Tabu search approach to the clustering problem , 1995, Pattern Recognit..

[36]  Ajith Abraham,et al.  Artificial immune system inspired behavior-based anti-spam filter , 2007, Soft Comput..

[37]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[38]  Leandro Nunes de Castro,et al.  An Immune and a Gradient-Based Method to Train Multi-Layer Perceptron Neural Networks , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[39]  James Ting-Ho A cortex-like learning machine for temporal hierarchical pattern clustering, detection, and recognition , 2012 .

[40]  Jonathan Timmis,et al.  A Comment on Opt-AiNET: An Immune Network Algorithm for Optimisation , 2004, GECCO.

[41]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[42]  M. Narasimha Murty,et al.  Genetic K-means algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[43]  HartEmma,et al.  Exploiting the Analogy between the Immune System and Sparse Distributed Memories , 2003 .

[44]  Sudipto Guha,et al.  ROCK: a robust clustering algorithm for categorical attributes , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[45]  Ickjai Lee,et al.  AUTOCLUST: Automatic Clustering via Boundary Extraction for Mining Massive Point-Data Sets , 2000 .