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 language | English (US) |
|---|---|
| Pages (from-to) | 142-152 |
| Number of pages | 11 |
| Journal | Decision Support Systems |
| Volume | 54 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2012 |
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Keywords
- Actionable behavioral rule
- Actionable knowledge discovery
- Data mining
ASJC Scopus subject areas
- Management Information Systems
- Information Systems
- Information Systems and Management
- Arts and Humanities (miscellaneous)
- Developmental and Educational Psychology
Cite this
Mining actionable behavioral rules. / Su, Peng; Mao, Wenji; Zeng, Dajun; Zhao, Huimin.
In: Decision Support Systems, Vol. 54, No. 1, 12.2012, p. 142-152.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Mining actionable behavioral rules
AU - Su, Peng
AU - Mao, Wenji
AU - Zeng, Dajun
AU - Zhao, Huimin
PY - 2012/12
Y1 - 2012/12
N2 - 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.
AB - 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.
KW - Actionable behavioral rule
KW - Actionable knowledge discovery
KW - Data mining
UR - http://www.scopus.com/inward/record.url?scp=84868649039&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868649039&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2012.04.013
DO - 10.1016/j.dss.2012.04.013
M3 - Article
AN - SCOPUS:84868649039
VL - 54
SP - 142
EP - 152
JO - Decision Support Systems
JF - Decision Support Systems
SN - 0167-9236
IS - 1
ER -