Presentation 2001/3/16
Fuzzy Clustering Based on Similarity Matrix
Kiichi URAHAMA, Kohei INOUE,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) A method is presented for fuzzy clustering of data based on similarity matrix. Fuzzy clustering is formulated by an optimization problem and its iterative solution procedure is derived. Extraction of each cluster is reduced to an eigenvalue problem. Clusters are extracted from a major one to minor ones and the number of clusters is estimated by the profile of total extraction degree of data. Approximate forms of cluster obtained by this sequential extraction process can be refined by cyclic iteration of cluster extraction to convergence. Performance of the method is examined by simple data and it is applied to segmentation of videos and still images.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) fuzzy clustering / similarity matrix / sequential iteration / eigen-decomposition
Paper # PRMU2000-236
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Conference Information
Committee PRMU
Conference Date 2001/3/16(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Fuzzy Clustering Based on Similarity Matrix
Sub Title (in English)
Keyword(1) fuzzy clustering
Keyword(2) similarity matrix
Keyword(3) sequential iteration
Keyword(4) eigen-decomposition
1st Author's Name Kiichi URAHAMA
1st Author's Affiliation Faculty of Visual Communication Design, Kyushu Institute of Design()
2nd Author's Name Kohei INOUE
2nd Author's Affiliation Faculty of Visual Communication Design, Kyushu Institute of Design
Date 2001/3/16
Paper # PRMU2000-236
Volume (vol) vol.100
Number (no) 702
Page pp.pp.-
#Pages 6
Date of Issue