| A heuristic method for finding the optimal number of clusters with application in medical data. | |
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MedLine Citation:
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PMID: 19163761 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In this paper, a heuristic method for determining the optimal number of clusters is proposed. Four clustering algorithms, namely K-means, Growing Neural Gas, Simulated Annealing based technique, and Fuzzy C-means in conjunction with three well known cluster validity indices, namely Davies-Bouldin index, Calinski-Harabasz index, Maulik-Bandyopadhyay index, in addition to the proposed index are used. Our simulations evaluate capability of mentioned indices in some artificial and medical datasets. |
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Authors:
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Hamidreza Bayati; Heydar Davoudi; Emad Fatemizadeh |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Volume: 2008 ISSN: 1557-170X ISO Abbreviation: Conf Proc IEEE Eng Med Biol Soc Publication Date: 2008 |
Date Detail:
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Created Date: 2009-02-16 Completed Date: 2009-05-11 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101243413 Medline TA: Conf Proc IEEE Eng Med Biol Soc Country: United States |
Other Details:
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Languages: eng Pagination: 4684-7 Citation Subset: IM |
Affiliation:
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Biological Signal and Image Processing Laboratory (BiSIPL), Department of Electrical Engineering, Sharif University of Technology, Iran. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Artificial Intelligence Cluster Analysis* Computer Simulation Data Interpretation, Statistical Fuzzy Logic Gene Expression Profiling Humans Medical Informatics / methods* Models, Genetic Models, Statistical Models, Theoretical Pattern Recognition, Automated / methods |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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