An improved genetic clustering algorithm for categorical data

Authors:
Hongwu Qin;Xiuqin Ma;Tutut Herawan;Jasni Mohamad Zain
Affiliations:
Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Gambang, Kuantan, Malaysia;Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Gambang, Kuantan, Malaysia;Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Gambang, Kuantan, Malaysia;Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Gambang, Kuantan, Malaysia
Venue:
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
Year:
2012

Citing 8
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k-ANMI: A mutual information based clustering algorithm for categorical data

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G-ANMI: A mutual information based genetic clustering algorithm for categorical data

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An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data

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Abstract

Deng et al. [Deng, S., He, Z., Xu, X.: G-ANMI: A mutual information based genetic clustering algorithm for categorical data, Knowledge-Based Systems 23, 144---149(2010)] proposed a mutual information based genetic clustering algorithm named G-ANMI for categorical data. While G-ANMI is superior or comparable to existing algorithms for clustering categorical data in terms of clustering accuracy, it is very time-consuming due to the low efficiency of genetic algorithm (GA). In this paper, we propose a new initialization method for G-ANMI to improve its efficiency. Experimental results show that the new method greatly improves the efficiency of G-ANMI as well as produces higher clustering accuracy.