B ibliography for Clementine Modeling Tools




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Bibliography for Clementine Modeling Tools




Apriori


Agrawal, R., Srikant, R. 1994. Fast Algorithms for Mining Association Rules.


Download from http://www.almaden.ibm.com/cs/quest/publications.html. This is one of a number of papers on association rule induction available at this site. This is a key paper for understanding Clementine’s Apriori modeling tool.


Berry, M.J.A., Linoff, G. 1997. Data Mining Techniques for Marketing, Sales, and Customer Support. New York: John Wiley and Sons.

See Chapter 8 on Market Basket Analysis.




Build C5.0



Berry, M.J.A., Linoff, G. 1997. Data Mining Techniques for Marketing, Sales, and Customer Support. New York: John Wiley and Sons.

See Chapter 12 on Decision Trees.


Mitchell, T. 1997. Machine Learning. Boston: McGraw-Hill.

See Chapter 3 on Decision Tree Learning.


Quinlan, R. 1993. C4.5: Programs for Machine Learning. San Mateo: Morgan Kaufmann Publishers.

Detailed description of C4.5 with source code listing.


Quinlan, R. http://www.rulequest.com/see5-comparison.html and http://www.rulequest.com/see5-win.html. The Rulequest website has some comments on C5.0 versus C4.5.


Quinlan, R. http://www.cse.unsw.edu.au/~quinlan/

Ross Quinlan’s academic website has a downloadable paper “Boosting, Bagging, and C4.5.”




C & RT



Berry, M.J. and G. Linoff. (2000). Mastering data mining: The art and science of

customer relationship management. Wiley, New York.


Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone. (1984). Classification and

regression trees. Wadsworth, Belmont, Calif.


Kass, G. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29:2. pp. 119–127.


Lim, T.S., W.Y. Loh, and Y.S. Shih. (2000). A comparison of prediction accuracy,

complexity, and training time of thirty-three old and new classification algorithms.

Machine Learning, 40.


Loh, W.Y., and Y.S. Shih. (1997). Split selection methods for classification trees.

Statistica Sinica, 7. pp. 815–840.





Factor Analysis/ Principal Components Analysis


Dziubin and Shirkey (1974)

Harman (1976)

Hendrickson and White (1964)

Jöreskog (1977)

Kaiser (1963)

Rummel (1970)


Darlington, Richard B., Sharon Weinberg, and Herbert Walberg (1973). Canonical variate analysis and related techniques. Review of Educational Research, 453-454.


Gorsuch, Richard L. (1983) Factor Analysis. Hillsdale, NJ: Erlbaum


Morrison, Donald F. (1990) Multivariate Statistical Methods. New York: McGraw-Hill.


Rubenstein, Amy S. (1986). An item-level analysis of questionnaire-type measures of intellectual curiosity. Cornell University Ph. D. thesis.




GRI



Berry, M.J.A., Linoff, G. 1997. Data Mining Techniques for Marketing, Sales, and Customer Support. New York: John Wiley and Sons.

See Chapter 8 on Market Basket Analysis.


Mallen, Bramer. 1995. “Cupid – Utilising Domain Knowledge in Knowledge Discovery.” Expert Systems XI.

Discusses a KDD system developed to utilize domain knowledge in induction from noisy datasets.


Smyth, P., Goodman, R. 1992. “An Information Approach to Rule Induction from Databases.” IEEE Transactions on Knowledge Engineering and Data Engineering, vol. 4, number 4. Proposes the J measure, an information-theoretic measure of “interestingness.”




Multinomial Logistic Regression



Agresti, A. 1990. Categorical Data Analysis. New York: John Wiley & Sons.


Agresti, A. 1996. An Introduction to Categorical Data Analysis. New York: John Wiley & Sons.


Collett, D. 1991. Modelling Binary Data. London: Chapman and Hall.


Cox, D.R. and Snell, E.J. 1989. The Analysis of Binary Data. 2nd ed. New York: John Wiley & Sons.


Hosmer, D.W. and Lemeshow, S. 1989. Applied Logistic Regression. New York: John Wiley & Sons.


McCullagh, P and Nelder, J.A. 1989. Generalized Linear Models. 2nd ed. London: Chapman and Hall.




Regression


Jain, D. 1994. “Regression Analysis for Marketing Decisions.” In Principles of Marketing Research, edited by Richard P. Bagozzi. Blackwell Publishers.

Discusses regression analysis from a market research perspective.




Train Kmeans



Arabie, P., and Hubert, H. 1994. “Cluster Analysis in Marketing Research.” In Advanced Methods of Marketing Research, edited by Richard P. Bagozzi. Blackwell Publishers.

Useful recent review of cluster analysis.


Berry, M.J.A., Linoff, G. 1997. Data Mining Techniques for Marketing, Sales, and Customer Support. New York: John Wiley and Sons.

See Chapter 10 on cluster analysis.




Train Kohonen



Berry, M.J.A., Linoff, G. 1997. Data Mining Techniques for Marketing, Sales, and Customer Support. New York: John Wiley and Sons.

See Chapter 13 on Artificial Neural Networks, especially the section on using neural networks for undirected data mining.


Martin-del-Brio, B., Serrano-Cinca, C. 1995. “Self-organizing Neural Networks: The Financial State of Spanish Companies.” In Neural Networks in the Capital Markets, edited by Apostolos-Paul Refenes. New York: John Wiley and Sons.

An application paper.




Train Net



Berry, M.J.A., Linoff, G. 1997. Data Mining Techniques for Marketing, Sales, and Customer Support. New York: John Wiley and Sons.

See Chapter 13 on Artificial Neural Networks.


Bigus, J.P. 1996. Data Mining with Neural Networks: Solving Business Problems—from Application Development to Decision Support. New York: McGraw-Hill.

Listed on the Neural Network FAQ as a good book for business executives.


Bishop, C.M. 1995. Neural Networks for Pattern Recognition. Oxford: Oxford University Press.

A standard reference for the statistician.


Masters, T. 1993. Practical Neural Network Recipes in C++. Academic Press.


Masters, T. 1995. Advanced Algorithms for Neural Networks: A C++ Sourcebook. New York: John Wiley and Sons.

You can read Masters’ two books in their own right even if you’re not interested in the code.


Ripley, B.D. 1996. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.

Another standard reference for the statistician. Discusses not only neural networks but other methods too.




Two Step Cluster


Banfield J. D. and A. E. Raftery. (1993). Model-based Gaussian and non-Gaussian clustering.

Biometrics, 49. p. 803–821.


Fraley C. and A.E. Raftery. (1998). How many clusters? Which clustering method?

Answers via model-based cluster analysis. Computer Journal, 4. p. 578–588.


Fraley, C. (1998). Algorithms for model-based Gaussian hierarchical clustering.

SIAM Journal on Scientific Computing, 20. p. 270–281.


Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with

categorical values. Data Mining and Knowledge Discovery, 2. p. 283–304.


Kaufman, L. and P.J. Rousseeuw. (1990). Finding groups in data: An introduction to

cluster analysis. Wiley, New York.


Melia, M. and D. Heckerman. (1998). An experimental comparison of several clustering

and initialization methods. Microsoft Research Technical Report MSR-TR-98-06.


Theodoridis, S. and K. Koutroumbas. (1999). Pattern recognition. Academic Press, New York.


Zhang, T., R. Ramakrishnon and M. Livny. (1996). BIRCH: An efficient data clustering

method for very large databases. Proceedings of the ACM SIGMOD Conference on

Management of Data.p. 103–114, Montreal, Canada.



ג'ניוס מערכות בע"מ, נציגת SPSS בישראל הסיבים 7, קרית מטלון ת.ד. 7796 פתח תקוה 49170 03-9222204 www.spss.com/Israel


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