research-article

A Cluster Separation Measure

Authors:

David L. Davies,

Donald W. BouldinAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 1, Issue 2

Pages 224 - 227

https://doi.org/10.1109/TPAMI.1979.4766909

Published: 01 February 1979 Publication History

Abstract

A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.

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Lin KZhou JZheng W(2025)Human-Centric Transformer for Domain Adaptive Action RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.342938747:2(679-696)Online publication date: 1-Feb-2025
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Escriva ELefrere TMartin MAligon JChanson AExcoffier JLabroche NSoulé-Dupuy CMonsarrat P(2025)Effective data exploration through clustering of local attributive explanationsInformation Systems10.1016/j.is.2024.102464127:COnline publication date: 1-Jan-2025
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A Cluster Separation Measure
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures

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cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 1, Issue 2

February 1979

116 pages

ISSN:0162-8828

Issue’s Table of Contents

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 February 1979

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