The mainstream data mining faces critical challenges and lacks of soft power in solving real-world complex problems when deployed. Following the paradigm shift from ‘data mining’ to ‘knowledge discovery’, we believe much more thorough efforts are essential for promoting the wide acceptance and employment of knowledge discovery in real-world smart decision making. To this end, we expect a new paradigm shift from ‘data-centered knowledge discovery’ to ‘domain-driven actionable knowledge discovery’. In the domain-driven actionable knowledge discovery, ubiquitous intelligence must be involved and meta-synthesized into the mining process, and an actionable knowledge discovery-based problem-solving system is formed as the space for data mining. This is the motivation and aim of developing Domain Driven Data Mining (D 3 M for short). This chapter briefs the main reasons, ideas and open issues in D 3 M.
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References
Ankerst, M.: Report on the SIGKDD-2002 Panel the Perfect Pata Mining Tool: Interactive or Automated? ACM SIGKDD Explorations Newsletter, 4(2):110–111, 2002.
Cao, L., Yu, P., Zhang, C., Zhao, Y., Williams, G.: DDDM2007: Domain Driven Data Mining, ACM SIGKDD Explorations Newsletter, 9(2): 84–86, 2007.
Cao, L., Zhang, C.: Knowledge Actionability: Satisfying Technical and Business Interesting-ness, International Journal of Business Intelligence and Data Mining, 2(4): 496–14, 2007.
Cao, L., Zhang, C.: The Evolution of KDD: Towards Domain-Driven Data Mining, International Journal of Pattern Recognition and Artificial Intelligence, 21(4): 677–692, 2007.
Cao, L.: Domain-Driven Actionable Knowledge Discovery, IEEE Intelligent Systems, 22(4): 78–89, 2007.
Cao, L., Dai, R., Zhou, M.: Metasynthesis, M-Space and M-Interaction for Open Complex Giant Systems, technical report, 2008.
Fayyad, U., Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases, AI Magazine, 37–54, 1996.
Fayyad, U., Shapiro, G., Uthurusamy, R.: Summary from the KDD-03 Panel — Data mining: The Next 10 Years, ACM SIGKDD Explorations Newsletter, 5(2): 191–196, 2003.
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edition, Morgan Kauf-mann, 2006.
Qian, X.S., Yu, J.Y., Dai, R.W.: A New Scientific Field—Open Complex Giant Systems and the Methodology, Chinese Journal of Nature, 13(1) 3–10, 1990.
Qian, X.S. (Tsien H.S.): Revisiting issues on open complex giant systems, Pattern Recognition and Artificial Intelligence, 4(1): 5–8, 1991.
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Longbing, C. (2009). Introduction to Domain Driven Data Mining. In: Cao, L., Yu, P.S., Zhang, C., Zhang, H. (eds) Data Mining for Business Applications. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79420-4_1
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DOI: https://doi.org/10.1007/978-0-387-79420-4_1
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