Advertisement

Data Mining: An Introduction

  • Ishwar K. Sethi
Part of the Massive Computing book series (MACO, volume 3)

Abstract

This chapter provides an introductory overview of data mining. Data mining, also referred to as knowledge discovery in databases, is concerned with nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases. The main focus of the chapter is on different data mining methodologies and their relative strengths and weaknesses.

Keywords

Data Mining Structure Query Language Data Mining Method Discriminant Score Decision Tree Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Breiman, L., Friedman, J., Olshen, R. and Stone, C.J., Classification and Regression Trees, Belmont, CA: Wadsworth Int’1 Group, 1984.Google Scholar
  2. Duda R. and Hart, P., Pattern Classification and Scene Analysis. New York: John Wiley and Sons, 1973.zbMATHGoogle Scholar
  3. Epstein J.M. and Axtell, R., Growing Artificial Societies. Washington, DC: Brookerings Institution Press, 1996.Google Scholar
  4. Fayyad U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining. Cambridge, MA: AAAI Press/MIT Press, 1996.Google Scholar
  5. Goldberg D.E., Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.zbMATHGoogle Scholar
  6. Hair Jr. J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., Multivariate Data Analysis. New York: Macmillan Publishing Company, 1987.Google Scholar
  7. Kohonen T., “Self-Organized Formation of Topologically Correct Feature Maps,” Biological Cybernetics, 43, pp. 59–69, 1982.MathSciNetCrossRefzbMATHGoogle Scholar
  8. Lu, H., Setiono, R. and Liu, H., “NeuroRule: A Connectionist Approach to Data Mining,” in Proceedings of the 2151 VLDB Conference, pp. 478–489, 1995.Google Scholar
  9. Looney C.G., Pattern Recognition Using Neural Networks. New York: Oxford University Press, 1997.Google Scholar
  10. Myers J.H., Segmentation and Positioning for Strategic Marketing Decisions. Chicago: American Marketing Association, 1996.Google Scholar
  11. Pawlak Z., Rough Sets: Theoretical Aspects of Reasoning About Data. Dordrecht, The Netherlands: Kluwer Academic Publishers, 1991.Google Scholar
  12. Quinlan J.R., “Induction of Decision Trees,” Machine Learning, 1, pp. 81–106, 1986.Google Scholar
  13. Rumelhart, D.E., Hinton, G.E., and William, R.J., “Learning Internal Representation by Error Propagation,” in Parallel Distributed Processing, MIT Press: Cambridge, MA, 1986.Google Scholar
  14. Sethi I.K. and Yoo, J.H., “Design of Multicategory Multifeature Split Decision Trees Using Perceptron Learning,” Pattern Recognition, 27 (7), pp. 939–947, 1994.CrossRefGoogle Scholar
  15. Sethi I.K. and Yoo, J.H., “Symbolic Mapping of Neurons in.Feedforward Networks,” Pattern Recognition Letters, 17 (10), pp. 1035–1046, 1996.CrossRefGoogle Scholar
  16. Sirat J.A. and Nadal, J.-P., “Neural Trees: A New Tool for Classification,” Networks, 1, pp. 423–438, 1990.MathSciNetCrossRefGoogle Scholar
  17. Weiss S.M. and Kulikowski, C.A., Computer Systems That Learn. San Mateo, CA: Morgan Kaufmann Publishers, 1991.Google Scholar
  18. Zadeh L.A., “Fuzzy Sets,” Information and Control, 8, pp. 338–353, 1965.Google Scholar
  19. Zurada J.M., Artificial Neural Systems. St. Paul, MN: West Publishing, 1992.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Ishwar K. Sethi
    • 1
  1. 1.Intelligent Information Engineering Laboratory, Department of Computer Science and EngineeringOakland UniversityRochesterUSA

Personalised recommendations