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Activity Number: 272 - Approaches in Clustering for Analysis of Emerging Data Types
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #322872
Title: On Measuring Soft Agreement in Clustering
Author(s): Jeffrey Andrews* and Ryan Browne and Chelsey Hvingelby
Companies: University of British Columbia Okanagan and University of Waterloo and Concordia University
Keywords: Clustering; Classification; Fuzzy; Agreement measures; Rand index; Mixture models

We will review some of the literature surrounding assessments of agreement between soft/fuzzy/probabilistic cluster allocations, and then provide 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 proposed measures retain the reflexive property of the ARI --- in other words, that an allocation measured against itself results in the value 1.0, an arguably essential property for the interpretability of a cluster agreement measure --- and both are feasible in their closed-form for sample sizes ranging beyond five digits. We apply these methods to real and simulated data to illustrate their utility and contrast with competing approaches.

Authors who are presenting talks have a * after their name.

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