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Mixture-Model Cluster Analysis Using Model Selection Criteria and a New Informational Measure of Complexity

In: Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach

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  • Hamparsum Bozdogan

    (The University of Tennessee, Department of Statistics)

Abstract

Analysis of clusters by means of mixture distribution, called mixture-model cluster analysis, has been one of the most difficult problems in statistics. But theoretical work, coupled with the development of new computational tools in the past ten years, has been made it possible to overcome some of the intractable technical and numerical issues that have limited the widespread applicability of mixture-model cluster analysis to complex real-word problems. The development of new objective analysis techniques had to wait the emergence of information-based model selection procedure to overcome difficulties with cinventional techniques within the context of the mixture-model cluster analysis. See, e.g., Bozdogan (1992), Windham and Cutler (1993) (in this volume)

Suggested Citation

  • Hamparsum Bozdogan, 1994. "Mixture-Model Cluster Analysis Using Model Selection Criteria and a New Informational Measure of Complexity," Springer Books, in: Hamparsum Bozdogan & Stanley L. Sclove & Arjun K. Gupta & D. Haughton & G. Kitagawa & T. Ozaki & K. (ed.), Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach, chapter 2, pages 69-113, Springer.
    • Handle: RePEc:spr:sprchp:978-94-011-0800-3_3
    DOI: 10.1007/978-94-011-0800-3_3
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