The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.09 - September (2010 vol.22)
pp: 1299-1312
Longbing Cao , University of Technology, Sydney, New South Wales
Yanchang Zhao , University of Technology, Sydney, New South Wales
Huaifeng Zhang , University of Technology, Sydney, New South Wales
Dan Luo , University of Technology, Sydney, New South Wales
Chengqi Zhang , University of Technology, Sydney, New South Wales
E.K. Park , College of Staten Island, City University of New York, Staten Island
ABSTRACT
Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this paper, we present a formal view of actionable knowledge discovery (AKD) from the system and decision-making perspectives. AKD is a closed optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and is designed to deliver operable business rules that can be seamlessly associated or integrated with business processes and systems. To support such processes, we correspondingly propose, formalize, and illustrate four types of generic AKD frameworks: Postanalysis-based AKD, Unified-Interestingness-based AKD, Combined-Mining-based AKD, and Multisource Combined-Mining-based AKD (MSCM-AKD). A real-life case study of MSCM-based AKD is demonstrated to extract debt prevention patterns from social security data. Substantial experiments show that the proposed frameworks are sufficiently general, flexible, and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making.
INDEX TERMS
Data mining, domain-driven data mining (D^3M), actionable knowledge discovery, decision making.
CITATION
Longbing Cao, Yanchang Zhao, Huaifeng Zhang, Dan Luo, Chengqi Zhang, E.K. Park, "Flexible Frameworks for Actionable Knowledge Discovery", IEEE Transactions on Knowledge & Data Engineering, vol.22, no. 9, pp. 1299-1312, September 2010, doi:10.1109/TKDE.2009.143
REFERENCES
[1] G. Adomavicius and A. Tuzhilin, "Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach," Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD '97), pp. 111-114, 1997.
[2] C. Aggarwal, "Towards Effective and Interpretable Data Mining by Visual Interaction," ACM SIGKDD Explorations Newsletter, vol. 3, no. 2, pp. 11-22, 2002.
[3] M. Ankerst, "Report on the SIGKDD-2002 Panel the Perfect Data Mining Tool: Interactive or Automated?" ACM SIGKDD Explorations Newsletter, vol. 4, no. 2, pp. 110-111, 2002.
[4] J.F. Boulicaut and B. Jeudy, "Constraint-Based Data Mining," The Data Mining and Knowledge Discovery Handbook, pp. 399-416, Springer, 2005.
[5] K. Breitman, M. Casanova, and W. Truszkowski, Semantic Web. Springer, 2007.
[6] L. Cao, "Domain-Driven Actionable Knowledge Discovery," IEEE Intelligent Systems, vol. 22, no. 4, pp. 78-89, July/Aug. 2007.
[7] L. Cao, "Domain-Driven Data Mining: Empowering Actionable Knowledge Delivery," Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD '09) Tutorial, 2009.
[8] L. Cao and Y. Ou, "Market Microstructure Pattern Analysis for Powering Trading and Surveillance Agents," J. Universal Computer Science, vol. 14, no. 14, pp. 2288-2308, 2008.
[9] L. Cao, "Developing Actionable Trading Strategies," Knowledge Processing and Decision Making in Agent-Based Systems, N. Nguyen and L. Jain, eds., Springer, 2008.
[10] L. Cao, P. Yu, C. Zhang, and H. Zhang, Data Mining for Business Applications. Springer, 2008.
[11] L. Cao, Y. Zhao, C. Zhang, and H. Zhang, "Activity Mining: From Activities to Actions," Int'l J. Information Technology and Decision Making, vol. 7, no. 2, pp. 259-273, 2008.
[12] L. Cao, P. Yu, C. Zhang, and Y. Zhao, Domain Driven Data Mining. Springer, 2009.
[13] L. Cao, Y. Zhao, and C. Zhang, "Mining Impact-Targeted Activity Patterns in Imbalanced Data," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 8, pp. 1053-1066, Aug. 2008.
[14] L. Cao and C. Zhang, "Knowledge Actionability: Satisfying Technical and Business Interestingness," Int'l J. Business Intelligence and Data Mining, vol. 2, no. 4, pp. 496-514, 2007.
[15] L. Cao and C. Zhang, "The Evolution of KDD: Towards Domain-Driven Data Mining," Int'l J. Pattern Recognition and Artificial Intelligence, vol. 21, no. 4, pp. 677-692, 2007.
[16] L. Cao and C. Zhang, "Fuzzy Genetic Algorithms for Pairs Mining," Proc. Pacific Rim Int'l Conf. Artificial Intelligence (PRICAI '06), pp. 711-720, 2006.
[17] L. Cao, R. Dai, and M. Zhou, "Metasynthesis: M-Space, M Interaction and M-Computing for Open Complex Giant Systems," IEEE Trans. Systems, Man, and Cybernetics-Part A, vol. 39, no. 5, pp. 1007-1021, Sept. 2009.
[18] L. Cao, H. Zhang, Y. Zhao, and C. Zhang, "Combined Mining: Discovering More Informative Knowledge in e-Government Services," technical report, Univ. of Technology Sydney, 2008.
[19] U. Fayyad, G. Shapiro, and R. Uthurusamy, "Summary from the KDD-03 Panel—Data mining: The Next 10 Years," ACM SIGKDD Explorations Newsletter, vol. 5, no. 2, pp. 191-196, 2003.
[20] U. Fayyad and P. Smyth, "From Data Mining to Knowledge Discovery: An Overview," Advances in Knowledge Discovery and Data Mining, U. Fayyad and P. Smyth, eds., pp. 1-34, AAAI Press/MIT Press, 1996.
[21] A. Freitas, "On Objective Measures of Rule Surprisingness," Proc. Second European Symp. Principles of Data Mining and Knowledge Discovery (PKDD '98), pp. 1-9, 1998.
[22] O.G. Ali and W. Wallace, "Bridging the Gap between Business Objectives and Parameters of Data Mining Algorithms," Decision Support Systems, vol. 21, pp. 3-15, 1997.
[23] H. Kargupta, B. Park, D. Hershbereger, and E. Johnson, "Collective Data Mining: A New Perspective toward Distributed Data Mining," Advances in Distributed Data Mining, H. Kargupta and P. Chan, eds., AAAI/MIT Press, 1999.
[24] J. Kleinberg, C. Papadimitriou, and P. Raghavan, "A Microeconomic View of Data Mining," Data Mining and Knowledge Discovery, vol. 2, no. 4, pp. 311-324, 1998.
[25] R. Hilderman and H. Hamilton, "Applying Objective Interestingness Measures in Data Mining Systems," Proc. Symp. Principles of Data Mining and Knowledge Discovery (PKDD), pp. 432-439, 2000.
[26] B. Lent, A.N. Swami, and J. Widom, "Clustering Association Rules," Proc. 13th Int'l Conf. Data Eng., pp. 220-231, 1997.
[27] B. Liu, W. Hsu, and Y. Ma, "Pruning and Summarizing the Discovered Associations," Proc. ACM SIGKDD, 1999.
[28] B. Liu and W. Hsu, "Post-Analysis of Learned Rules," Proc. Nat'l Conf. Artificial Intelligence/Innovative Applications of Artificial Intelligence Conf. (AAAI/IAAI), 1996.
[29] B. Liu, W. Hsu, S. Chen, and Y. Ma, "Analyzing Subjective Interestingness of Association Rules," IEEE Intelligent Systems, vol. 15, no. 5, pp. 47-55, Sept./Oct. 2000.
[30] E. Omiecinski, "Alternative Interest Measures for Mining Associations," IEEE Trans. Knowledge and Data Eng., vol. 15, no. 1, pp. 57-69, Jan./Feb. 2003.
[31] B. Padmanabhan and A. Tuzhilin, "A Belief-Driven Method for Discovering Unexpected Patterns," Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 94-100, 1998.
[32] B. Park and H. Kargupta, "Distributed Data Mining: Algorithms and Systems, Applications," Data Mining Handbook, pp. 341-358, 2002.
[33] A. Silberschatz and A. Tuzhilin, "What Makes Patterns Interesting in Knowledge Discovery Systems," IEEE Trans. Knowledge and Data Eng., vol. 8, no. 6, pp. 970-974, Dec. 1996.
[34] A. Silberschatz and A. Tuzhilin, "On Subjective Measures of Interestingness in Knowledge Discovery," Proc. Int'l Conf. Knowledge Discovery and Data Mining, pp. 275-281, 1995.
[35] P. Tan, V. Kumar, and J. Srivastava, "Selecting the Right Interestingness Measure for Association Patterns," Proc. ACM SIGKDD, pp. 32-41, 2002.
[36] A. Tzacheva and Z. Ras, "Action Rules Mining," Int'l J. Intelligent Systems, vol. 20, no. 7, pp. 719-736, 2005.
[37] K. Wang, S. Zhou, and J. Han, "Profit Mining: From Patterns to Actions," Proc. Int'l Conf. Extending Database Technology (EBDT), 2002.
[38] Q. Yang, J. Yin, C. Ling, and R. Pan, "Extracting Actionable Knowledge from Decision Trees," IEEE Trans. Knowledge and Data Eng., vol. 19, no. 1, pp. 43-56, Jan. 2007.
[39] Y. Yao and Y. Zhao, "Explanation-Oriented Data Mining," Encyclopedia of Data Warehousing and Mining, J. Wang, ed., pp. 492-497, 2005.
[40] S. Yoon, L. Henschen, E. Park, and S. Makki, "Using Domain Knowledge in Knowledge Discovery," Proc. Eighth Int'l Conf. Information and Knowledge Management, pp. 243-250, 1999.
[41] H. Zhang, Y. Zhao, L. Cao, C. Zhang, and H. Bohlscheid, "Customer Activity Sequence Classification for Debt Prevention in Social Security," J. Computer Science and Technology, vol. 24, no. 6, pp. 1000-1009, 2009.
[42] Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, Y. Zhao, C. Zhang, and L. Cao, eds. IGI Press, 2008.
[43] Y. Zhao, H. Zhang, L. Cao, C. Zhang, and Y. Ou, "Data Mining Application in Social Security Data," Data Mining for Business Applications, L. Cao, P. Yu, C. Zhang, and H. Zhang, eds., Springer, 2008.
7 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool