Negative encoding length as a subjective interestingness measure for groups of rules

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

4 被引用数 (Scopus)

抄録

We propose an interestingness measure for groups of classification rules which are mutually related based on the Minimum Description Length Principle. Unlike conventional methods, our interestingness measure is based on a theoretical background, has no parameter, is applicable to a group of any number of rules, and can exploit an initial hypothesis. We have integrated the interestingness measure with practical heuristic search and built a rule-group discovery method CLARDEM (Classification Rule Discovery method based on an Extended-Mdlp).Extensive experiments using both real and artificial data confirm that CLARDEM can discover the correct concept from a small noisy data set and an approximate initial concept with high "discovery accuracy".

本文言語英語
ホスト出版物のタイトル13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
ページ220-231
ページ数12
DOI
出版ステータス出版済み - 7 23 2009
イベント13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, タイ
継続期間: 4 27 20094 30 2009

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)
5476 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Countryタイ
CityBangkok
Period4/27/094/30/09

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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