Schema design for causal law mining from incomplete database

Kazunori Matsumoto, Kazuo Hashimoto

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

The paper describes the causal law mining from an incomplete database. First we extend the definition of association rules in order to deal with uncertain attribute values in records. As Agrawal’s well-know algorithm generates too many irrelevant association rules, a filtering technique based on minimal AIC principle is applied here. The graphic representation of association rules validated by a filter may have directed cycles. The authors propose a method to exclude useless rules with a stochastic test, and to construct Bayesian networks from the remaining rules. Finally, a schem for Causal Law Mining is proposed as an integration of the techniques described in the paper.

本文言語English
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版社Springer Verlag
ページ92-102
ページ数11
1721
ISBN(印刷版)354066713X, 9783540667131
DOI
出版ステータスPublished - 1999
外部発表はい
イベント2nd International Conference on Discovery Science, DS 1999 - Tokyo, Japan
継続期間: 1999 12 61999 12 8

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)
1721
ISSN(印刷版)03029743
ISSN(電子版)16113349

Other

Other2nd International Conference on Discovery Science, DS 1999
国/地域Japan
CityTokyo
Period99/12/699/12/8

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 理論的コンピュータサイエンス

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