Recommended Literature

Introduction to Data Mining

Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann.

Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Amsterdam, Netherlands: Academic Press.

Classification

Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Belmont, CA: Wadsworth International.

Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification. New York, NY: Wiley-Interscience.

Quinlan, J. (1993). C4.5: Programs for machine learning. San Francisco, CA: Morgan Kaufmann.

Cluster Analysis

Anderberg, M. (1973). Cluster analysis for applications. Amsterdam, Netherlands: Academic Press.

Association Rules

Adamo, J. (2000). Data mining for association rules and sequential patterns: Sequential and parallel algorithms. Berlin, Germany: Springer Verlag.

Neural Nets

Hertz, J. A., Krogh, A. S., & Palmer, R. G. (1991). Introduction to the theory of neural computation. Reading, MA: Addison-Wesley.

Genetic Algorithms

Michalewicz, Z. (1996). Genetic algorithms + data structure = evolution programs. Berlin, Germany: Springer Verlag.

Data Mining in Business Applications

Berry, M. J., & Linoff, G. S. (1999). Mastering data mining: The art and science of customer relationship management. New York, NY: Wiley.

Berry, M. J., & Linoff, G. S. (2004). Data mining techniques: For marketing, sales, and customer relationship management. Indianapolis, IN: Wiley.

Rud, O. (2000). Data mining cookbook: Modeling data for marketing, risk and customer relationship management. New York, NY: Wiley.

Case Study (Market Basket Analysis for a Chain Store)

Corinne, B. (2001). Mining your own business in retail using DB2 intelligent miner or data. IBM Form No. SG24-6271-00. IBM Redbooks.