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Journal of Classification
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11-07-2019

Suboptimal Comparison of Partitions

Authors: Jonathon J. O’Brien, Michael T. Lawson, Devin K. Schweppe, Bahjat F. Qaqish

Published in: Journal of Classification | Issue 2/2020

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Abstract

The distinction between classification and clustering is often based on a priori knowledge of classification labels. However, in the purely theoretical situation where a data-generating model is known, the optimal solutions for clustering do not necessarily correspond to optimal solutions for classification. Exploring this divergence leads us to conclude that no standard measures of either internal or external validation can guarantee a correspondence with optimal clustering performance. We provide recommendations for the suboptimal evaluation of clustering performance. Such suboptimal approaches can provide valuable insight to researchers hoping to add a post hoc interpretation to their clusters. Indices based on pairwise linkage provide the clearest probabilistic interpretation, while a triplet-based index yields information on higher level structures in the data. Finally, a graphical examination of receiver operating characteristics generated from hierarchical clustering dendrograms can convey information that would be lost in any one number summary.
previous article A Theoretical Analysis of the Peaking Phenomenon in Classification
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Metadata
Title
Suboptimal Comparison of Partitions
Authors
Jonathon J. O’Brien
Michael T. Lawson
Devin K. Schweppe
Bahjat F. Qaqish
Publication date
11-07-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 2/2020
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-019-09329-1

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