Model Based Clustering Using Evolutionary Algorithm
Clustering is collection of data objects that are similar to one another and thus can be treated collectively as one group. The model based clustering approach uses model for clustering and optimizes the fit between the data and model. The evolutionary algorithm has the ability to thoroughly search...
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Published in | I-Manager's Journal on Information Technology Vol. 2; no. 4; pp. 16 - 20 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Nagercoil
iManager Publications
15.11.2013
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Abstract | Clustering is collection of data objects that are similar to one another and thus can be treated collectively as one group. The model based clustering approach uses model for clustering and optimizes the fit between the data and model. The evolutionary algorithm has the ability to thoroughly search the parameter space, providing an approach inherently more robust with respect to local maxima. In EvolvExpectation-Maximization(EvolvEM)algorithm,Expectation Maximization and Genetic algorithm is used for clustering data which shows more efficiency then EM clustering. The drawback in this method is that its execution time is higher and it requires more parameters. In the proposed approach, instead of Genetic algorithm, Bee colony optimization can be combined with Expectation Maximization algorithm in order to improve execution time and clustering efficiency. Hence, it can be efficiently used for clustering. |
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AbstractList | Clustering is collection of data objects that are similar to one another and thus can be treated collectively as one group. The model based clustering approach uses model for clustering and optimizes the fit between the data and model. The evolutionary algorithm has the ability to thoroughly search the parameter space, providing an approach inherently more robust with respect to local maxima. In EvolvExpectation-Maximization(EvolvEM)algorithm,Expectation Maximization and Genetic algorithm is used for clustering data which shows more efficiency then EM clustering. The drawback in this method is that its execution time is higher and it requires more parameters. In the proposed approach, instead of Genetic algorithm, Bee colony optimization can be combined with Expectation Maximization algorithm in order to improve execution time and clustering efficiency. Hence, it can be efficiently used for clustering. |
Author | Priya, D. R. Radhika, R. A. |
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Cites_doi | 10.1016/j.csda.2007.02.009 10.1016/j.patrec.2013.02.008 10.1007/s11222-008-9072-0 10.1109/TPAMI.2005.162 |
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Copyright | Copyright iManager Publications Sep-Nov 2013 |
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CorporateAuthor | Department of Information Technology, Kongu Engineering College, Perundurai, India Assistant Professor, Dept of Information Technology, Kongu Engineering College, Perundurai, India |
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References | ref0 Franz Pernkopf (ref2) 2005; 27 Dimitris Karlis (ref1) 2009; 19 ref3 Jeffrey L. Andrews (ref4) 2013; 34 |
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Title | Model Based Clustering Using Evolutionary Algorithm |
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