research-article

Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

Author:

C. T. ZahnAuthors Info & Claims

IEEE Transactions on Computers, Volume 20, Issue 1

Pages 68 - 86

https://doi.org/10.1109/T-C.1971.223083

Published: 01 January 1971 Publication History

Abstract

A family of graph-theoretical algorithms based on the minimal spanning tree are capable of detecting several kinds of cluster structure in arbitrary point sets; description of the detected clusters is possible in some cases by extensions of the method. Development of these clustering algorithms was based on examples from two-dimensional space because we wanted to copy the human perception of gestalts or point groupings. On the other hand, all the methods considered apply to higher dimensional spaces and even to general metric spaces. Advantages of these methods include determinacy, easy interpretation of the resulting clusters, conformity to gestalt principles of perceptual organization, and invariance of results under monotone transformations of interpoint distance. Brief discussion is made of the application of cluster detection to taxonomy and the selection of good feature spaces for pattern recognition. Detailed analyses of several planar cluster detection problems are illustrated by text and figures. The well-known Fisher iris data, in four-dimensional space, have been analyzed by these methods also. PL/1 programs to implement the minimal spanning tree methods have been fully debugged.

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Index Terms

Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Computers

IEEE Transactions on Computers Volume 20, Issue 1

January 1971

137 pages

ISSN:0018-9340

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Copyright © 1971.

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IEEE Computer Society

United States

Publication History

Published: 01 January 1971

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https://dl.acm.org/doi/10.1145/3673038.3673148
Li AMao YZhang JDai Y(2024)Mutual Information Regularization for Weakly-Supervised RGB-D Salient Object DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.328524934:1(397-410)Online publication date: 1-Jan-2024
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