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Information Technology Journal
  Year: 2012 | Volume: 11 | Issue: 10 | Page No.: 1409-1417
DOI: 10.3923/itj.2012.1409.1417
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A Fast Evolutionary Algorithm for Automatic Evolution of Clusters

Singh Vijendra, K. Ashiwini and Sahoo Laxman

This paper proposed an evolutionary clustering algorithm which can automatically determine the number of clusters present in a data set. The chromosomes are represented as strings of real numbers, encode the centers of a fixed number of clusters. The searching capability of evolutionary clustering 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 proposed clustering approach called Fast Automatic Clustering Evolution (FACE) in data set. To obtain a speedup over linear search in high dimensional data a randomized k-d trees based nearest neighbor search is used. The chromosomes are able to exchange their gene values according to nearest cluster centers and relation among genes in crossover operator. Mutation operator replaced the mutation gene value with respect to nearest neighbor cluster. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GA (Genetic Algorithm) to a local optimum. The Adjusted-Rand Index is used as a measure of the validity of the clusters. Effectiveness of the proposed algorithm is demonstrated for both artificial and real-life data sets. The experimental result demonstrates that the proposed clustering algorithm (FACE) has high performance, effectiveness and flexibility. The proposed evolutionary algorithm is able for clustering low to high dimensional data set.
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How to cite this article:

Singh Vijendra, K. Ashiwini and Sahoo Laxman, 2012. A Fast Evolutionary Algorithm for Automatic Evolution of Clusters. Information Technology Journal, 11: 1409-1417.

DOI: 10.3923/itj.2012.1409.1417






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