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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| Language: | en |
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Elsevier
2014
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| Online Access: | http://umpir.ump.edu.my/id/eprint/7511/1/MGR-%20An%20Information%20Theory%20Based%20Hierarchical%20Divisive%20Clustering%20Algorithm%20for%20Categorical%20Data.pdf http://umpir.ump.edu.my/id/eprint/7511/ http://dx.doi.org/10.1016/j.knosys.2014.03.013 |
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| 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 |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.7511 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2014 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | my.ump.umpir.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. (Published) http://dx.doi.org/10.1016/j.knosys.2014.03.013 DOI: 10.1016/j.knosys.2014.03.013 |
| 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 |
| title | 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_short | 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 |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/7511/1/MGR-%20An%20Information%20Theory%20Based%20Hierarchical%20Divisive%20Clustering%20Algorithm%20for%20Categorical%20Data.pdf http://umpir.ump.edu.my/id/eprint/7511/ http://dx.doi.org/10.1016/j.knosys.2014.03.013 |
| url_provider | http://umpir.ump.edu.my/ |