Advertisement

Data mining for risk analysis and targeted marketing

  • G. Jha
  • S.C. Hui
Knowledge Discovery and Data Mining
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1531)

Abstract

Commerical databases often contain critical business information concerning past performance which could be used to predict the future. However, the huge amounts of data can make the extraction of this business information almost impossible by manual methods or standard software techniques. Data mining techniques can analyze, understand and visualize the huge amounts ofstored data gathered from business applications and thus help companies sta stored data gathered from business applications and thus help companies stay competitive in today’s marketplace. Currently, a number of data mining applications and prototypes have been developed for a variety of business domains. Most of these applications are targeted at predictive modeling that finds pattern of data to help predict the future trend and behaviors of some entities. Apart from predictive modeling, other data mining tasks such as summarization, association, classification and clustering could also be applied to business databases. In this paper, we will illustrate the different data mining tasks applied to a real-life business database for risk analysis and targeted marketing.

Keywords

Data mining knowledge discovery in databases data mining process risk analysis targeted marketing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M.S. Chen, J. Han and P.S. Yu, “Data Mining: An overview from a database perspective”, IEEE Transactions on Knowledge and Data Engineering, 8, 6 (Dec. 1996) 866–883.CrossRefGoogle Scholar
  2. 2.
    U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “From Data Mining to Knowledge Discovery: An overview” In Advances in Knowledge Discovery and Data Mining U.M. Fayyad, G. Piatetsky-Shapiro P. Smyth and R. Uthurusamy, Eds. AAAI Press/The MIT Press, Cambridge, Mass., 1996, 1–34.Google Scholar
  3. 3.
    U. M. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “The KDD Process for extracting useful knowledge from volumes of data” Communications of the ACM, 39, 11 (Nov. 1996), 27–34.CrossRefGoogle Scholar
  4. 4.
    R.J. Brachman et al., “Mining Business Databases”, Communication of the ACM, pp. 42–48.Google Scholar
  5. 5.
    J. Han, “OLAP Mining: An Integration of OLAP with Data Mining”, Proc. 1997 IFIP Conference on Data Semantics (DS-7), Leysin, Switzerland, Oct. 1997, pp. 1–11.Google Scholar
  6. 6.
    J. Han, Y. Cai, N. cercone. Data-driven discovery of quantitative rules in relational databases. IEEE Trans. Knowledge and Data Engineering, 5:29–40, 1993.CrossRefGoogle Scholar
  7. 7.
    R. Agrawal and R. Srikant. Fast Algorithms for mining association rules. In Proc. 1994 Int. Conf. Very Large Data Bases, pp. 487–499, Santiago, Chile, September 1994.Google Scholar
  8. 8.
    J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. In Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data, pp. 175–186, San Jose, CA, May 1995.Google Scholar
  9. 9.
    A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proc. 1995 Int. Conf. Very Large Data Bases, pp. 432–443, Zürich, Switzerland, Sept. 1995.Google Scholar
  10. 10.
    R. Srikant and R. Agrawal. Mining Generalized Association Rules. Proceedings of the 21″ International Conference on Very Large Data Bases, pages 407–419, September 1995.Google Scholar
  11. 11.
    Y. Fu and J. Han. Meta-rule-guided mining of association rules in relational databases. In Proc. 1″ Int’l Workshop on Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD ’95), pages 39–46, Singapore, Dec. 1995.Google Scholar
  12. 12.
    M.S. Chen, J.S. Park, and P.S. Yu. Data Mining for Path Traversal Patterns in a Web Environment. Proceedings of the 16″ International Conference on Distributed Computing Systems, papges 385–392, May 27–30 1996.Google Scholar
  13. 13.
    D.W. Cheung, J. Han, V. Ng, and C.Y. Wong. Maintenance of discovered association rules in large databases: An incremental updating technique. In Proc. 1996 Int’l Conf. On Data Engineering, New Orleans, Louisiana, Feb. 1996.Google Scholar
  14. 14.
    J.S. Park, M.S. Chen, and P.S. Yu. Efficient parallel mining for association rules. In Proc. 4 th Int. Conf. On Information and Knowledge Management, pp. 31–36, Baltimore, Maryland, Nov. 1995.Google Scholar
  15. 15.
    Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs”, Springer, 1996.Google Scholar
  16. 16.
    J. Elder IV and D. Pregibon, “A statistical perspective on knowledge discovery in databases” In Advances in Knowledge Discovery and Data Mining. U.M. Fayyad, G. Piatetsky-Shapiro P. Smyth and R. Uthurusamy, Eds. AAAI Press/The MIT Press, Cambridge, Mass., 1996, pp. 83–115.Google Scholar
  17. 17.
    W. Ziarko, “Rough Sets, Fuzzy Sets and Knowledge Discovery”, Sporinger-Verlag, 1994.Google Scholar
  18. 18.
    L. Breiman, J. Friedman, R. Olshen and C. Stone, “Classification of Regression Trees” Wadsworth, 1984.Google Scholar
  19. 19.
    J.R. Quinlan, “Induction of Decision Trees” Machine Learning, 1:81–106, 1986.Google Scholar
  20. 20.
    J.R. Quinlan, “C4.5: Programs for Machine Learning,” Morgan Kaufmann, 1993.Google Scholar
  21. 21.
    B. V. Dasarathy, “Nearest Neighbour (NN) Norms: NN Pattern Classification Techniques”, Los Alamitos, CA, IEEE Computer Society Press, 1991.Google Scholar
  22. 22.
    J. Kolodner, “Case-Based Reasoning”, Morgan Kaufmann, 1993.Google Scholar
  23. 23.
    H. Lu, R. Setiono and H. Liu, “Effective Data Mining Using Neural Networks”, IEEE Transactions on Knowledge and Data Engineering”, Vol. 8, No. 6, Dec. 1996, pp 957–961.CrossRefGoogle Scholar
  24. 24.
    J.P. Bigus, “Data Mining with Neural Networks: Solving Business Problems-From Application Development to Decision Support” New York: McGraw-Hill, 1996.Google Scholar
  25. 25.
    R.S. Michalski, “A theory and methodlogy of inductive learning”, in Michalski et al. (editor), Machine Learning: An Artificial Intelligence Approach, Vol. 1, pp 83–134, Morgan Kaufmann, 1983.Google Scholar
  26. 26.
    P. Clark and T. Niblett, “The CN2 Induction Algorithm. Machine Learning” 3(4), pp 261–283 1989.Google Scholar
  27. 27.
    M. Mehta, R. Agrawal, and J. Rissanen “SLIQ: A fast scalable classifier for data mining”, In Proc. 1996 International Conferencve on Extending Database Technology (EDBT ’96), Avignon France, March 1996.Google Scholar
  28. 28.
    J. Shafer, R. Agrawal and M. Mehta, “SPLINT: a scalable parallel classifier for data mining”, in Proc. 22nd Intl. Conf. Very Large Dta Bases (VLDB), pp 544–555, India, 1996.Google Scholar
  29. 29.
    M. Kamber et. al. “Generalisation and Decision Tree Induction: Efficient Classification in Data Mining”, Proc. of 1997 Intl. Workshop on Research Issues on Data Engineering (RIDE ’97), Birmingham, England, April 1997, pp. 111–120.Google Scholar
  30. 30.
    D. Fisher. Improving infereence through conceptual clustering. In Proc. 1987 AAAI Conf., pp. 461–465, Seattle, Washington, July 1987.Google Scholar
  31. 31.
    R. Ng and J. Han. Efficient and effective clustering method for spatial data mingin. In Proc. 1994 Int. Conf. Very Large Data Bases, pp. 144–155, Santiago, Chile, September 1994.Google Scholar
  32. 32.
    T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: an efficient data clustering method for very large databases. In Proc 1996 ACM-SIGMOD Int. Conf. Management of Data, Montreal, Canada, June 1996.Google Scholar
  33. 33.
    M. Ester, H. P. Kriegel, and X. Xu. Knowledge discovery in large spatial databases: Focussing techniques for efficient class identification. In Proc. 4th Int. Symp. On Large Spatial Databases (SSD’95), pp. 67–82, Portland, Maine, August 1995.Google Scholar
  34. 34.
    J. Han et al, “DBMiner: A System for Data Mining in Relational Databases and Data Warehouses”, Proc CASCON ’97 Meeting of Minds, Toronto, Canada, Nov 1997.Google Scholar
  35. 35.
    DBMiner System, Online document available at URL: http:/db.cs.sfu.ca/DBMiner.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • G. Jha
    • 1
  • S.C. Hui
    • 1
  1. 1.School of Applied ScienceNanyang Technological UniversitySingapore

Personalised recommendations