MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data

MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data

Categorical data clustering has attracted much attention recently due to the fact that much of the data contained in today’s databases is categorical in nature. While many algorithms for clustering categorical data have been proposed, some have low clustering accuracy while others have high computat...

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Main Authors: Hongwu, Qin, Ma, Xiuqin, Herawan, Tutut, Jasni, Mohamad Zain
Format: Article
Language:English
Published: Elsevier 2014
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/7511/
http://umpir.ump.edu.my/id/eprint/7511/
http://umpir.ump.edu.my/id/eprint/7511/
http://umpir.ump.edu.my/id/eprint/7511/1/MGR-%20An%20Information%20Theory%20Based%20Hierarchical%20Divisive%20Clustering%20Algorithm%20for%20Categorical%20Data.pdf
id ump-7511
recordtype eprints
spelling ump-75112018-05-18T02:51:40Z http://umpir.ump.edu.my/id/eprint/7511/ MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data Hongwu, Qin Ma, Xiuqin Herawan, Tutut Jasni, Mohamad Zain QA76 Computer software Categorical data clustering has attracted much attention recently due to the fact that much of the data contained in today’s databases is categorical in nature. While many algorithms for clustering categorical data have been proposed, some have low clustering accuracy while others have high computational complexity. This research proposes mean gain ratio (MGR), a new information theory based hierarchical divisive clustering algorithm for categorical data. MGR implements clustering from the attributes viewpoint which includes selecting a clustering attribute using mean gain ratio and selecting an equivalence class on the clustering attribute using entropy of clusters. It can be run with or without specifying the number of clusters while few existing clustering algorithms for categorical data can be run without specifying the number of clusters. Experimental results on nine University of California at Irvine (UCI) benchmark and ten synthetic data sets show that MGR performs better as compared to baseline algorithms in terms of its performance and efficiency of clustering. Elsevier 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/7511/1/MGR-%20An%20Information%20Theory%20Based%20Hierarchical%20Divisive%20Clustering%20Algorithm%20for%20Categorical%20Data.pdf Hongwu, Qin and Ma, Xiuqin and Herawan, Tutut and Jasni, Mohamad Zain (2014) MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data. Knowledge-Based Systems, 67. pp. 401-411. ISSN 0950-7051 http://dx.doi.org/10.1016/j.knosys.2014.03.013 DOI: 10.1016/j.knosys.2014.03.013
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Hongwu, Qin
Ma, Xiuqin
Herawan, Tutut
Jasni, Mohamad Zain
MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data
description Categorical data clustering has attracted much attention recently due to the fact that much of the data contained in today’s databases is categorical in nature. While many algorithms for clustering categorical data have been proposed, some have low clustering accuracy while others have high computational complexity. This research proposes mean gain ratio (MGR), a new information theory based hierarchical divisive clustering algorithm for categorical data. MGR implements clustering from the attributes viewpoint which includes selecting a clustering attribute using mean gain ratio and selecting an equivalence class on the clustering attribute using entropy of clusters. It can be run with or without specifying the number of clusters while few existing clustering algorithms for categorical data can be run without specifying the number of clusters. Experimental results on nine University of California at Irvine (UCI) benchmark and ten synthetic data sets show that MGR performs better as compared to baseline algorithms in terms of its performance and efficiency of clustering.
format Article
author Hongwu, Qin
Ma, Xiuqin
Herawan, Tutut
Jasni, Mohamad Zain
author_facet Hongwu, Qin
Ma, Xiuqin
Herawan, Tutut
Jasni, Mohamad Zain
author_sort Hongwu, Qin
title MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data
title_short MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data
title_full MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data
title_fullStr MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data
title_full_unstemmed MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data
title_sort mgr: an information theory based hierarchical divisive clustering algorithm for categorical data
publisher Elsevier
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/7511/
http://umpir.ump.edu.my/id/eprint/7511/
http://umpir.ump.edu.my/id/eprint/7511/
http://umpir.ump.edu.my/id/eprint/7511/1/MGR-%20An%20Information%20Theory%20Based%20Hierarchical%20Divisive%20Clustering%20Algorithm%20for%20Categorical%20Data.pdf
first_indexed 2023-09-18T22:04:11Z
last_indexed 2023-09-18T22:04:11Z
_version_ 1777414597291016192

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