Skip to main content
SpringerLink
Log in
Book cover

Pacific Rim International Conference on Artificial Intelligence

PRICAI 1998: PRICAI’98: Topics in Artificial Intelligence pp 158–169Cite as

Data mining for risk analysis and targeted marketing

  • Knowledge Discovery and Data Mining
  • Conference paper
  • First Online:

  • 185 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  4. R.J. Brachman et al., “Mining Business Databases”, Communication of the ACM, pp. 42–48.

    Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs”, Springer, 1996.

    Google Scholar 

  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. W. Ziarko, “Rough Sets, Fuzzy Sets and Knowledge Discovery”, Sporinger-Verlag, 1994.

    Google Scholar 

  18. L. Breiman, J. Friedman, R. Olshen and C. Stone, “Classification of Regression Trees” Wadsworth, 1984.

    Google Scholar 

  19. J.R. Quinlan, “Induction of Decision Trees” Machine Learning, 1:81–106, 1986.

    Google Scholar 

  20. J.R. Quinlan, “C4.5: Programs for Machine Learning,” Morgan Kaufmann, 1993.

    Google Scholar 

  21. B. V. Dasarathy, “Nearest Neighbour (NN) Norms: NN Pattern Classification Techniques”, Los Alamitos, CA, IEEE Computer Society Press, 1991.

    Google Scholar 

  22. J. Kolodner, “Case-Based Reasoning”, Morgan Kaufmann, 1993.

    Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. P. Clark and T. Niblett, “The CN2 Induction Algorithm. Machine Learning” 3(4), pp 261–283 1989.

    Google Scholar 

  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. 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. 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. D. Fisher. Improving infereence through conceptual clustering. In Proc. 1987 AAAI Conf., pp. 461–465, Seattle, Washington, July 1987.

    Google Scholar 

  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. 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. 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. 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. DBMiner System, Online document available at URL: http:/db.cs.sfu.ca/DBMiner.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Applied Science, Nanyang Technological University, Nanyang Avenue, 639798, Singapore

    G. Jha & S.C. Hui

Authors
  1. G. Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S.C. Hui
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Hing-Yan Lee Hiroshi Motoda

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jha, G., Hui, S. (1998). Data mining for risk analysis and targeted marketing. In: Lee, HY., Motoda, H. (eds) PRICAI’98: Topics in Artificial Intelligence. PRICAI 1998. Lecture Notes in Computer Science, vol 1531. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095266

Download citation

  • DOI: https://doi.org/10.1007/BFb0095266

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65271-7

  • Online ISBN: 978-3-540-49461-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation