An Improved Genetic Clustering Algorithm for Categorical Data

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 comp...

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Main Authors: Jasni, Mohamad Zain, Qin, Hongwu, Ma, Xiuqin, Herawan, Tutut
Other Authors: Washio, Takashi
Format: Book Chapter
Language:English
Published: Springer 2013
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/6186/
http://umpir.ump.edu.my/id/eprint/6186/1/PAKDD13.pdf
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author Jasni, Mohamad Zain
Qin, Hongwu
Ma, Xiuqin
Herawan, Tutut
author2 Washio, Takashi
author_facet Washio, Takashi
Jasni, Mohamad Zain
Qin, Hongwu
Ma, Xiuqin
Herawan, Tutut
author_sort Jasni, Mohamad Zain
building UMP Institutional Repository
collection Online Access
description 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.
first_indexed 2025-11-15T01:26:12Z
format Book Chapter
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institution Universiti Malaysia Pahang
institution_category Local University
language English
last_indexed 2025-11-15T01:26:12Z
publishDate 2013
publisher Springer
recordtype eprints
repository_type Digital Repository
spelling ump-61862018-05-21T05:18:21Z http://umpir.ump.edu.my/id/eprint/6186/ An Improved Genetic Clustering Algorithm for Categorical Data Jasni, Mohamad Zain Qin, Hongwu Ma, Xiuqin Herawan, Tutut QA75 Electronic computers. Computer science 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. Springer Washio, Takashi Luo, Jun 2013 Book Chapter PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6186/1/PAKDD13.pdf 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 http://dx.doi.org/10.1007/978-3-642-36778-6_9 DOI: 10.1007/978-3-642-36778-6_9
spellingShingle QA75 Electronic computers. Computer science
Jasni, Mohamad Zain
Qin, Hongwu
Ma, Xiuqin
Herawan, Tutut
An Improved Genetic Clustering Algorithm for Categorical Data
title An Improved Genetic Clustering Algorithm for Categorical Data
title_full An Improved Genetic Clustering Algorithm for Categorical Data
title_fullStr An Improved Genetic Clustering Algorithm for Categorical Data
title_full_unstemmed An Improved Genetic Clustering Algorithm for Categorical Data
title_short An Improved Genetic Clustering Algorithm for Categorical Data
title_sort improved genetic clustering algorithm for categorical data
topic QA75 Electronic computers. Computer science
url http://umpir.ump.edu.my/id/eprint/6186/
http://umpir.ump.edu.my/id/eprint/6186/
http://umpir.ump.edu.my/id/eprint/6186/
http://umpir.ump.edu.my/id/eprint/6186/1/PAKDD13.pdf