On Assessments of Agreement Between Fuzzy Partitions

Jeffrey L. Andrews (), Ryan Browne () and Chelsey D. Hvingelby
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Jeffrey L. Andrews: University of British Columbia – Okanagan Campus
Ryan Browne: University of Waterloo
Chelsey D. Hvingelby: University of British Columbia – Okanagan Campus

Journal of Classification, 2022, vol. 39, issue 2, No 7, 326-342

Abstract: Abstract We extend the literature regarding assessments of agreement between soft/fuzzy/probabilistic cluster allocations by providing closed-form approaches for two measures which behave as fuzzy generalizations of the popular adjusted Rand index (ARI): one novel and one previously requiring a Monte Carlo estimation process. Both of these measures retain the reflexive property of the ARI—an arguably essential property for the interpretability of a cluster agreement measure—and both are feasible in their closed-form for sample sizes ranging into five digits or more using standard consumer computers. We describe the approximate computational complexity in each case, and apply both measures in simulated and real data contexts.

Keywords: Cluster analysis; Adjusted Rand index; Fuzzy partitions; Fuzzy c-means; Mixture models (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00357-021-09407-3

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