Extraction of fuzzy clusters from weighted graphs

Seiji Hotta, Kohei Inoue, Kiichi Urahama

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

3 被引用数 (Scopus)

抄録

A spectral graph method is presented for partitioning of nodes in a graph into fuzzy clusters on the basis of weighted adjacency matrices. Extraction of a fuzzy cluster from a node set is formulated by an eigenvalue problem and clusters are extracted sequentially from major one to minor ones. A clustering scheme is devised at first for undirected graphs and it is next extended to directed graphs and also to undirected bipartite ones. These clustering methods are applied to analysis of a link structure in Web networks and image retrieval queried by keywords or sample images. Extracted structure of clusters is visualized by a multivariate exploration method called the correspondence analysis.

本文言語英語
ホスト出版物のタイトルKnowledge Discovery and Data Mining
ホスト出版物のサブタイトルCurrent Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings
編集者Takao Terano, Huan Liu, Arbee L.P. Chen
出版社Springer Verlag
ページ442-453
ページ数12
ISBN(印刷版)3540673822, 9783540673828
DOI
出版ステータス出版済み - 2000
イベント4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 - Kyoto, 日本
継続期間: 4月 18 20004月 20 2000

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
1805
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000
国/地域日本
CityKyoto
Period4/18/004/20/00

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

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