An Improved Genetic Clustering Algorithm for Categorical Data

Jasni, Mohamad Zain and Qin, Hongwu and Ma, Xiuqin and Herawan, Tutut (2013) An Improved Genetic Clustering Algorithm for Categorical Data. In: Emerging Trends in Knowledge Discovery and Data Mining: PAKDD 2012 International Workshops: DMHM, GeoDoc, 3Clust, and DSDM, Kuala Lumpur, Malaysia, May 29 – June 1, 2012, Revised Selected Papers. Lecture Notes in Computer Science, 7769 (Lectur). Springer, Berlin Heidelberg, pp. 100-111. ISBN 978-3-642-36778-6

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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.

Item Type:	Book Chapter
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division:	Faculty of Computer System And Software Engineering
Depositing User:	Users 134 not found.
Date Deposited:	04 Aug 2014 04:27
Last Modified:	21 May 2018 05:18
URI:	http://umpir.ump.edu.my/id/eprint/6186
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