Discovering action rules that are highly achievable from massive data

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages713-722
Number of pages10
DOIs
Publication statusPublished - Jul 23 2009
Event13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand
Duration: Apr 27 2009Apr 30 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5476 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
CountryThailand
CityBangkok
Period4/27/094/30/09

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)

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 proceedingConference 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
Suzuki E. Discovering action rules that are highly achievable from massive data. In 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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