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Clustering data set with categorical feature using multi objective genetic algorithm

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3 Author(s)
Dutta, D. ; Dept. of Comput. Sci. & Inf. Technol., Univ. Inst. of Technol., Golapbug, India ; Dutta, P. ; Sil, J.

In the paper, real coded multi objective genetic algorithm based K-clustering method has been studied where K represents the number of clusters known apriori. The searching power of Genetic Algorithm (GA) is exploited to search for suitable clusters and cluster modes so that intra-cluster distance (Homogeneity, H) and inter-cluster distances (Separation, S) are simultaneously optimized. It is achieved by measuring H and S using Mod distance per feature metric, suitable for categorical features (attributes). We have selected 3 benchmark data sets from UCI Machine Learning Repository containing categorical features only. Here, K-modes is hybridized with GA to combine global searching capabilities of GA with local searching capabilities of K-modes. Considering context sensitivity, we have used a special crossover operator called “pairwise crossover” and “substitution”.

Published in:

Data Science & Engineering (ICDSE), 2012 International Conference on

Date of Conference:

18-20 July 2012