Knowledge actionability: satisfying technical and business interestingness

Longbing Cao, Dan Luo, Chengqi Zhang
2007 International Journal of Business Intelligence and Data Mining  
Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical
more » ... ss and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.
doi:10.1504/ijbidm.2007.016385 fatcat:kaqnldbu5be5tii3uwjjpk5aqm