TY - JOUR
T1 - Mining actionable behavioral rules
AU - Su, Peng
AU - Mao, Wenji
AU - Zeng, Daniel
AU - Zhao, Huimin
N1 - Funding Information:
This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 60921061 , 61175040 , 71025001 , 90924302 and 91024030 , and the Research Fund of State Key Laboratory of Management and Control for Complex Systems under Grant No. 20110102 .
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
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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 -