The Dunn index (DI) (introduced by J. C. Dunn in 1974) is a metric for evaluating clustering algorithms.[1][2] This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index, in that it is an internal evaluation scheme, where the result is based on the clustered data itself. As do all other such indices, the aim is to identify sets of clusters that are compact, with a small variance between members of the cluster, and well separated, where the means of different clusters are sufficiently far apart, as compared to the within cluster variance. For a given assignment of clusters, a higher Dunn index indicates better clustering. One of the drawbacks of using this is the computational cost as the number of clusters and dimensionality of the data increase.
^Dunn, J. C. (1973-09-17). "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters". Journal of Cybernetics. 3 (3): 32–57. doi:10.1080/01969727308546046. S2CID 120919314.
^Dunn, J. C. (1973-09-01). "Well-Separated Clusters and Optimal Fuzzy Partitions". Journal of Cybernetics. 4 (1) (published 1974): 95–104. doi:10.1080/01969727408546059. ISSN 0022-0280.
The Dunnindex (DI) (introduced by J. C. Dunn in 1974) is a metric for evaluating clustering algorithms. This is part of a group of validity indices including...
index is considered the best algorithm based on this criterion. Dunnindex The Dunnindex aims to identify dense and well-separated clusters. It is defined...
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theorem Dummy variable (statistics) Duncan's new multiple range test Dunnindex Dunnett's test Durbin test Durbin–Watson statistic Dutch book Dvoretzky–Kiefer–Wolfowitz...
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used metrics for cluster validity are the silhouette index, Davies-Bouldin index, Dunn'sindex, or Hubert's Γ {\displaystyle \Gamma } statistic. Class...
ISBN 978-0-330-41879-9. Dunn 2005, pp. 313–314; Mattock 1981 Dunn 2005, pp. 63–64; Elad 1987 Dunn 2005, p. 179; Janicsek 1929 Dunn 2005, p. 134 Note 17 Dunn 2005, p. 180...
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