Discovering action rules that are highly achievable from massive data

Einoshin Suzuki

doi:10.1007/978-3-642-01307-2_72

Discovering action rules that are highly achievable from massive data

Einoshin Suzuki

Intelligence Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages	713-722
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-01307-2_72
Publication status	Published - Jul 23 2009
Event	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand Duration: Apr 27 2009 → Apr 30 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5476 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/Territory	Thailand
City	Bangkok
Period	4/27/09 → 4/30/09

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-01307-2_72

Cite this

Suzuki, E. (2009). Discovering action rules that are highly achievable from massive data. In 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); Vol. 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); Vol. 5476 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Suzuki, E 2009, Discovering action rules that are highly achievable from massive data. in 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), vol. 5476 LNAI, pp. 713-722, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, Bangkok, Thailand, 4/27/09. https://doi.org/10.1007/978-3-642-01307-2_72

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Discovering action rules that are highly achievable from massive data

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