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Single Cluster Clustering

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

Abstract

  • Various approaches to comparing subsets are discussed.

  • Two approaches to direct single cluster clustering are described: seriation and moving center separation, which are reinterpreted as locally optimal algorithms for particular (mainly approximational) criteria.

  • A moving center algorithm is based on a novel concept of reference point: the cluster size depends on its distance from the reference point.

  • Five single cluster structures are considered in detail:
    • Principal cluster as related to both seriation and moving center;

    • Ideal fuzzy type cluster as modeling “ideal type” concept;

    • Additive cluster as related to the average link seriation;

    • Star cluster as a kind of cluster in a “non-geometrical” environment;

    • Box cluster as a pair of interconnected subsets.

  • Approximation framework is shown quite convenient in both extending the algorithms to multi cluster clustering (overlapping permitted) and interpreting.

Keywords

Local Search Minimum Span Tree Local Search Algorithm Star Cluster Additive Cluster 
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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