Abstract—This paper propose a new clustering algorithm (GACR) based on genetic algorithm. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centers in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centers of a fixed number of clusters. A chromosome reorganization method is proposed, which may effectively remove the degeneracy for purpose of more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GA to a local optimum. The features of this algorithm are investigated and the performance is evaluated experimentally using real and synthetic datasets with K-means and GCA .The experimental result demonstrates that the GACR clustering algorithm has high performance, effectiveness and flexibility.
Index Terms—Adaptive probabilities, Clustering, Evolutionary computation, Genetic algorithm
Vijendra Singh is with Faculty of Engineering and Technology, Mody Institute of Technology and Science, Lakshmangarh, Sikar, Rajasthan, India (phone: 919829668880; email: firstname.lastname@example.org ).
Laxman Sahoo is with NIEC, Luck now, UP, India (email: email@example.com.).
Ashwini Kelkar is with Faculty of Engineering and Technology, Mody Institute of Technology and Science ,Lakshmangarh, Sikar, Rajasthan, India.
Cite: Singh Vijendra, Sahoo Laxman and Kelkar Ashwini, "Mining Clusters in Data Sets of Data Mining: An Effective Algorithm," International Journal of Computer Theory and Engineering vol. 3, no. 1, pp. 171-177, 2011.