Feature Selection and Clustering References

In addition to standard textbooks, there are many published papers on these topics. The Institute for Information Technology of the Canadian National Research Council* maintains a useful bibliography on feature selection. For more information about clustering on the Web, visit the Classification Society of North America.
  1. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York, 1981). A clearly written monograph that emphasizes fuzzy clustering.

  2. P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice-Hall International, Englewood Cliffs, NJ, 1982). A very clear presentation of the mathematical foundations.

  3. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (Wiley-Interscience, New York, 1973). Chapter 6 treats clustering.

  4. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Ed. (Academic Press, New York, 1990. A standard, widely-used textbook.

  5. A. D. Gordon, Classification. Methods for Exploratory Analysis of Multivariate Data (Chapman and Hall, London, 1981). A statistically-oriented book on clustering.

  6. T. Kohonen, Self-Organizing Maps (Springer Verlag, Berlin, 1995). Kohononen invented the clustering approach known as self-organizing feature maps, inspired by the retinatopic, tonotopic, and somatatopic maps found in the brain.

  7. B. D. Ripley, Pattern Recognition and Neural Networks (Cambridge University Press, Cambridge, 1996). An excellent modern treatment for those with a strong background in statistics.

  8. H. Ritter, T. Martinetz and K. Schulten, Neural Computation and Self-Organizing Maps (Addison-Wesley, Reading, MA, 1992). Provides many interesting applications of Kohonen's self-organizing feature maps.

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* The Interacting with Modelled Environments Group of the NRC Institute for Information Technology is specifically concerned with HCI, and provides links to other HCI groups around the world.

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