S8 illustrates some of the difficulties inherent with cluster analysis; its aim is to alert investigators to the fact that various algorithms can suggest radically different substructures in the same data set. The balance of Chapter 3 concerns objective functional methods based on fuzzy c-partitions of finite data. The nucleus for all these methods is optimization of nonlinear objectives involving the weights u ik ; functionals using these weights will be differentiable over M fc —but not over M c —a decided advantage for the fuzzy embedding of hard c-partition space. Classical first- and second-order conditions yield iterative algorithms for finding the optimal fuzzy c-partitions defined by various clustering criteria.


Feature Selection Hiatal Hernia Cluster Center Fuzzy Cluster Cluster Criterion 
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.


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Copyright information

© Plenum Press, New York 1981

Authors and Affiliations

  • James C. Bezdek
    • 1
  1. 1.Utah State UniversityLoganUSA

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