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General C-means clustering model.

Jian Yu1

  • 1School of Computer Science & Information Technology, Beijing Jiaotong University, Beijing, 100044, P.R. China. jianyu@center.njtu.edu.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2005
PubMed
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This study introduces the general c-means clustering model (GCM) and establishes a novel link between Occam's razor and partitional clustering algorithms. It provides a framework and guide for developing effective clustering methods.

Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Partitional clustering is a key area within cluster analysis, with numerous algorithms developed.
  • Occam's razor, a principle favoring simplicity, is crucial for data-based models like partitional clustering.

Purpose of the Study:

  • To present a unifying generative framework for partitional clustering algorithms.
  • To establish the first-time connection between Occam's razor and partitional clustering.
  • To provide a theoretical guide for designing and implementing clustering algorithms.

Main Methods:

  • A novel definition of the mean is used to introduce the general c-means clustering model (GCM).
  • Local optimality tests of the GCM are employed to establish the link with Occam's razor.

Related Experiment Videos

  • A comprehensive review of existing objective function-based clustering algorithms is conducted using the GCM framework.

    • Main Results:

    • The general c-means clustering model (GCM) is presented as a unifying framework.
    • A significant connection between Occam's razor and partitional clustering is demonstrated.
    • A theoretical guide for developing clustering algorithms is discovered.

    Conclusions:

    • The GCM provides a unified perspective on partitional clustering algorithms.
    • The established link between Occam's razor and partitional clustering offers new insights.
    • The theoretical guide aids in the systematic development of clustering algorithms, validated by experiments.