A study of Bayesian clustering of a document set based on GA

Keiko Aoki, Kazunori Matsumoto, Keiichiro Hoashi, Kazuo Hashimoto

研究成果: Conference contribution

抄録

In this paper, we propose new approximate clustering algorithm that improves the precision of a top-down clustering. Top-down clustering is proposed to improve the clustering speed by Iwayama et al, where the cluster tree is generated by sampling some documents, making a cluster from these, assigning other documents to the nearest node and if the number of assigned documents is large, continuing sampling and clustering from top to down. To improve precision of the top-down clustering method, we propose selecting documents by applying a GA to decide a quasi-optimum layer and using a MDL criteria for evaluating the layer structure of a cluster tree.

本文言語English
ホスト出版物のタイトルSimulated Evolution and Learning - 2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998, Selected Papers
編集者Bob McKay, Xin Yao, Charles S. Newton, Jong-Hwan Kim, Takeshi Furuhashi
出版社Springer Verlag
ページ260-267
ページ数8
ISBN(印刷版)3540659072, 9783540659075
DOI
出版ステータスPublished - 1999
外部発表はい
イベント2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998 - Canberra, Australia
継続期間: 1998 11 241998 11 27

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)
1585
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998
国/地域Australia
CityCanberra
Period98/11/2498/11/27

ASJC Scopus subject areas

  • 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(全般)

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