Discovery of surprising exception rules based on intensity of implication

Einoshin Suzuki, Yves Kodratoff

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

39 被引用数 (Scopus)

抄録

This paper presents an algorithm for discovering surprising exception rules from data sets. An exception rule, which is defined as a deviational pattern to a common sense, exhibits unexpectedness and is sometimes extremely useful. A domain-independent approach, PEDRE, exists for the simultaneous discovery of exception rules and their common sense rules. However, PEDRE, being too conservative, have difficulty in discovering surprising rules. Historic exception discoveries show that surprise is often linked with interestingness. In order to formalize this notion we propose a novel approach by improving PEDRE. First, we reformalize the problem and settle a looser constraints on the reliability of an exception rule. Then, in order to screen out uninteresting rules, we introduce, for an exception rule, an evaluation criterion of surprise by modifying intensity of implication, which is based on significance. Our approach has been validated using data sets from the UCI repository.

本文言語英語
ホスト出版物のタイトルPrinciples of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings
編集者Jan M. Zytkow, Mohamed Quafafou
出版社Springer Verlag
ページ10-18
ページ数9
ISBN(印刷版)3540650687, 9783540650683
DOI
出版ステータス出版済み - 1998
外部発表はい
イベント2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998 - Nantes, フランス
継続期間: 9 23 19989 26 1998

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)
1510
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998
Countryフランス
CityNantes
Period9/23/989/26/98

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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