Automatic Clustering with Differential Evolution Using Cluster Number Oscillation Method

In this paper, an improved Differential Evolution algorithm (ACDE-O) with cluster number oscillation for automatic crisp clustering has been presented. The proposed algorithm needs no prior knowledge of the number of clusters of the data. Rather, it finds the optimal number of clusters on the processing with stable and fast convergence, cluster number oscillation mechanism will search more possible cluster number in case of bad initial cluster number caused bad clusters. Superiority of the proposed algorithm is demonstrated by comparing it with one recently developed partitional clustering algorithm. Experimental results over three real life datasets and the performance of proposed algorithm is mostly better than the other one.

[1]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

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

[3]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[4]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[5]  R. Krishnapuram,et al.  A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[6]  Sandra Paterlini,et al.  High performance clustering with differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[7]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[8]  Ujjwal Maulik,et al.  Performance Evaluation of Some Clustering Algorithms and Validity Indices , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Ujjwal Maulik,et al.  Genetic clustering for automatic evolution of clusters and application to image classification , 2002, Pattern Recognit..

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

[11]  Kevin D. Reilly,et al.  SEQOPTICS: a protein sequence clustering system , 2006, BMC Bioinformatics.

[12]  Andries P. Engelbrecht,et al.  Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification , 2007 .