Schema design for causal law mining from incomplete database

Kazunori Matsumoto, Kazuo Hashimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages92-102
Number of pages11
Volume1721
ISBN (Print)354066713X, 9783540667131
DOIs
Publication statusPublished - 1999
Externally publishedYes
Event2nd International Conference on Discovery Science, DS 1999 - Tokyo, Japan
Duration: 1999 Dec 61999 Dec 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1721
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Conference on Discovery Science, DS 1999
CountryJapan
CityTokyo
Period99/12/699/12/8

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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    Matsumoto, K., & Hashimoto, K. (1999). Schema design for causal law mining from incomplete database. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1721, pp. 92-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1721). Springer Verlag. https://doi.org/10.1007/3-540-46846-3_9