Knowledge actionability: satisfying technical and business interestingness

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.

[1]  Longbing Cao,et al.  Agent services-based infrastructure for online assessment of trading strategies , 2004 .

[2]  Jiawei Han,et al.  Profit Mining: From Patterns to Actions , 2002, EDBT.

[3]  Ke Wang,et al.  Mining Actionable Patterns by Role Models , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[4]  William A. Wallace,et al.  Bridging the gap between business objectives and parameters of data mining algorithms , 1997, Decis. Support Syst..

[5]  Alex Alves Freitas,et al.  A critical review of multi-objective optimization in data mining: a position paper , 2004, SKDD.

[6]  Gediminas Adomavicius,et al.  Discovery of Actionable Patterns in Databases: the Action Hierarchy Approach , 1997, KDD.

[7]  Howard J. Hamilton,et al.  Applying Objective Interestingness Measures in Data Mining Systems , 2000, PKDD.

[8]  David Taniar,et al.  Domain-Driven, Actionable Knowledge Discovery , 2007, IEEE Intelligent Systems.

[9]  Balaji Padmanabhan,et al.  A Belief-Driven Method for Discovering Unexpected Patterns , 1998, KDD.

[10]  Chengqi Zhang,et al.  Mining Impact-Targeted Activity Patterns in Imbalanced Data , 2008, IEEE Transactions on Knowledge and Data Engineering.

[11]  Ke Wang,et al.  Item selection by "hub-authority" profit ranking , 2002, KDD.

[12]  Chengqi Zhang,et al.  The Evolution of KDD: towards Domain-Driven Data Mining , 2007, Int. J. Pattern Recognit. Artif. Intell..

[13]  Qiang Yang,et al.  Postprocessing decision trees to extract actionable knowledge , 2003, Third IEEE International Conference on Data Mining.

[14]  Longbing Cao,et al.  Actionable Knowledge Discovery , 2009 .

[15]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[16]  Alexander Tuzhilin,et al.  Knowledge evaluation: Other evaluations: usefulness, novelty, and integration of interesting news measures , 2002 .

[17]  H. White,et al.  Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap , 1999 .

[18]  Ann Q. Gates,et al.  TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005 .

[19]  Mihael Ankerst,et al.  Human Involvement and Interactivity of the Next Generation’s Data Mining Tools , 2001 .

[20]  Chengqi Zhang,et al.  Fuzzy genetic algorithms for pairs mining , 2006 .

[21]  Chengqi Zhang,et al.  Intelligence Metasynthesis in Building Business Intelligence Systems , 2006, WImBI.

[22]  Balaji Padmanabhan,et al.  Unexpectedness as a Measure of Interestingness in Knowledge Discovery , 1999, Decis. Support Syst..

[23]  Raymond Chi-Wing Wong,et al.  MPIS: maximal-profit item selection with cross-selling considerations , 2003, Third IEEE International Conference on Data Mining.

[24]  Li Lin,et al.  Mining in-depth patterns in stock market , 2008, Int. J. Intell. Syst. Technol. Appl..

[25]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[26]  Chengqi Zhang,et al.  Domain-Driven Data Mining: A Practical Methodology , 2006, Int. J. Data Warehous. Min..

[27]  Alex Alves Freitas,et al.  On Objective Measures of Rule Surprisingness , 1998, PKDD.

[28]  Abraham Silberschatz,et al.  On Subjective Measures of Interestingness in Knowledge Discovery , 1995, KDD.

[29]  Angelina A. Tzacheva,et al.  Action rules mining: Research Articles , 2005 .

[30]  Jon M. Kleinberg,et al.  A Microeconomic View of Data Mining , 1998, Data Mining and Knowledge Discovery.

[31]  Wynne Hsu,et al.  Analyzing the Subjective Interestingness of Association Rules , 2000, IEEE Intell. Syst..

[32]  Victor S. Sheng,et al.  Maximum profit mining and its application in software development , 2006, KDD '06.

[33]  Angelina A. Tzacheva,et al.  Action rules mining , 2005, Int. J. Intell. Syst..

[34]  Charu C. Aggarwal,et al.  Towards effective and interpretable data mining by visual interaction , 2002, SKDD.

[35]  Maureen O'Hara,et al.  Market Microstructure Theory , 1995 .