Mining actionable behavioral rules

Peng Su, Wenji Mao, Daniel Zeng, Huimin Zhao

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

Many applications can benefit from constructing models to predict the behavior of an entity. However, such models do not provide the user with explicit knowledge that can be directly used to influence (restrain or encourage) behavior for the user's interest. Undoubtedly, the user often exactly needs such knowledge. This type of knowledge is called actionable knowledge. Actionability is a very important criterion measuring the interestingness of mined patterns. In this paper, to mine such knowledge, we take a first step toward formally defining a new class of data mining problem, named actionable behavioral rule mining. Our definition explicitly states the problem as a search problem in a framework of support and expected utility. We also propose two algorithms for mining such rules. Our experiment shows the validity of our approach, as well as the practical value of our defined problem.

Original languageEnglish (US)
Pages (from-to)142-152
Number of pages11
JournalDecision Support Systems
Volume54
Issue number1
DOIs
StatePublished - Dec 1 2012

Keywords

  • Actionable behavioral rule
  • Actionable knowledge discovery
  • Data mining

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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