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Sampling Methods for Building Initial Partitions

  • Z. Volkovich
  • J. Kogan
  • C. Nicholas

Summary

The initialization of iterative clustering algorithms is a difficult yet important problem in the practice of data mining. In this chapter, we discuss two new approaches for building such initial partitions. The first approach applies a procedure for selecting appropriate samples in the spirit of the Cross-Entropy (CE) method, and the second is based on a sequential summarizing schema. In the first approach, we use a sequential sample clustering procedure instead of the simulation step of the CE method. In this context, we state several facts related to the Projection Pursuit methodology for exploring the structure of a high-dimensional data set. In addition we review several external and internal approaches for cluster validity testing. Experimental results for cluster initializations obtained via the CE method and the first of the presented methods are reported for a real data set.

Keywords

Gaussian Mixture Model Projection Pursuit Initial Partition Noncentrality Parameter Projection Index 
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

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Z. Volkovich
    • 1
  • J. Kogan
    • 2
    • 3
  • C. Nicholas
    • 3
  1. 1.Software Engineering DepartmentORT Braude Academic CollegeKarmielIsrael
  2. 2.Department of Mathematics and StatisticsUniversity of MarylandBaltimore County, BaltimoreUSA
  3. 3.Department of Computer Science and Electrical EngineeringUniversity of MarylandBaltimore County, BaltimoreUSA

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