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Knowledge actionability: satisfying technical and business interestingness
by Longbing Cao, Dan Luo, Chengqi Zhang
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 2, No. 4, 2007

 

Abstract: 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 interestingness 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.

Online publication date: Sun, 23-Dec-2007

 

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