Discovering action rules that are highly achievable from massive data

研究成果: 著書/レポートタイプへの貢献会議での発言

2 引用 (Scopus)

抄録

In this paper, we propose a novel algorithm which discovers a set of action rules for converting negative examples into positive examples. Unlike conventional action rule discovery methods, our method AARUDIA (Achievable Action RUle DIscovery Algorithm) considers the effects of actions and the achievability of the class change for disk-resident data. In AARUDIA, effects of actions are specified using domain rules and the achievability is inferred with Naive Bayes classifiers. AARUDIA takes a new breadth-first search method which manages actionable literals and stable literals, and exploits the achievability to reduce the number of discovered rules. Experimental results with inflated real-world data sets are promising and demonstrate the practicality of AARUDIA.

元の言語英語
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713-722
ページ数10
DOI
出版物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(印刷物)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
タイ
Bangkok
期間4/27/094/30/09

Fingerprint

Breadth-first Search
Naive Bayes Classifier
Classifiers
Search Methods
Experimental Results
Demonstrate
Class

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

これを引用

Suzuki, E. (2009). Discovering action rules that are highly achievable from massive data. : 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 (pp. 713-722). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 5476 LNAI). https://doi.org/10.1007/978-3-642-01307-2_72

Discovering action rules that are highly achievable from massive data. / Suzuki, Einoshin.

13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 713-722 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻 5476 LNAI).

研究成果: 著書/レポートタイプへの貢献会議での発言

Suzuki, E 2009, Discovering action rules that are highly achievable from massive data. : 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 巻. 5476 LNAI, pp. 713-722, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, Bangkok, タイ, 4/27/09. https://doi.org/10.1007/978-3-642-01307-2_72
Suzuki E. Discovering action rules that are highly achievable from massive data. : 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 713-722. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-01307-2_72
Suzuki, Einoshin. / Discovering action rules that are highly achievable from massive data. 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. pp. 713-722 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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