UMP Institutional Repository

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

[img] PDF
PAKDD13.pdf - Published Version
Restricted to Repository staff only

Download (194kB) | Request a copy

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 Section
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
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item