Title: Knowledge actionability: satisfying technical and business interestingness

Authors: Longbing Cao, Dan Luo, Chengqi Zhang

Addresses: Faculty of Information Technology, University of Technology, Sydney, Australia. ' Faculty of Information Technology, University of Technology, Sydney, Australia. ' Faculty of Information Technology, University of Technology, Sydney, Australia

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.

Keywords: data mining; actionable knowledge; technical interestingness; business decision making; domain driven data mining; profit mining.

DOI: 10.1504/IJBIDM.2007.016385

International Journal of Business Intelligence and Data Mining, 2007 Vol.2 No.4, pp.496 - 514

Published online: 23 Dec 2007 *

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