Assessing the Quality of Fuzzy Partitions Using Relative Intersection(Computation and Computational Models)
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概要
- 論文の詳細を見る
In this paper, conventional validity indexes are reviewed and the shortcomings of the fuzzy cluster validation index based on intercluster proximity are examined. Based on these considerations, a new cluster validity index is proposed for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index is defined as the average value of the relative intersections of all possible pairs of fuzzy clusters in the system. It computes the overlap between two fuzzy clusters by considering the intersection of each data point in the overlap. The optimal number of clusters is obtained by minimizing the validity index with respect to c. Experiments in which the proposed validity index and several conventional validity indexes were applied to well known data sets highlight the superior qualities of the proposed index.
- 社団法人電子情報通信学会の論文
- 2005-03-01
著者
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KIM Dae
Department of Anatomy and Center for Advanced Medical Education by BK21 Project, Inha University Col
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LEE Kwang
Department of Sericulture and Entomology, National Institute of Agriculture and Technology
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Kim Dae
Department Of Anatomy And Center For Advanced Medical Education By Bk21 Project Inha University Coll
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Kim Dae
Department Of Biosystems And Advanced Information Technology Research Center
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Kim Youngil
Department Of Electrical Engineering & Computer Science
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LEE Doheon
Department of BioSystems and Advanced Information Technology Research Center
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Lee Kwang
Department Of Bioengineering Yonsei University
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Lee Kwang
Department Of Biosystems And Advanced Information Technology Research Center:department Of Electrica
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Lee Kwang
Department of Advanced Technology Fusion (DATF), Konkuk University, 1 Hwayang-dong, Gwangjin-gu, Seoul 143-701, Korea
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