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Partition: Rectangular Data Table

  • Boris Mirkin
Chapter
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 11)

Abstract

  • Bilinear clustering for mixed — quantitative, nominal and binary — variables is proved to be a theory-motivated extension of K-Means method.

  • Decomposition of the data scatter into “explained” and “residual” parts is provided (for each of the two norms: sum of squares and moduli).

  • Contribution weights are derived to attack machine learning problems (conceptual description, selecting and transforming the variables, and knowledge discovery).

  • The explained data scatter parts related to nominal variables appear to coincide with the chi-squared Pearson coefficient and some other popular indices, as well.

  • Approximation (bi)-partitioning for contingency tables substantiates and extends some popular clustering techniques.

Keywords

Fuzzy Cluster Data Scatter Precision Error Cluster Partition Bilinear Model 
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

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Boris Mirkin
    • 1
  1. 1.DIMACSRutgers UniversityUSA

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