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April 1, 1979IEEE Transactions on Pattern Analysis and Machine Intelligence9,068 citations

A Cluster Separation Measure

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DDDavid L. DaviesDBDonald W. Bouldin

Key Points

  • The aim is to introduce a measure for evaluating the similarity of clusters based on data density.
  • Developed a novel measure indicating the similarity of data clusters.
  • Assessed how well this measure infers data partition appropriateness.
  • Ensured the measure is independent of the number of clusters and partitioning methods.
  • The measure successfully evaluated various data partitions, indicating appropriateness.
  • It provided insights into the clustering process without being affected by the method used.
  • The measure can guide protocols for cluster seeking algorithms effectively.

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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Cite This Study

Davies et al. (1979) studied this question.

synapsesocial.com/papers/69d739b25f9a1dad5348f748https://doi.org/10.1109/tpami.1979.4766909
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