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Communication Dans Un Congrès Année : 2022

A Distributional Approach for Soft Clustering Comparison and Evaluation

Résumé

The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms. In this article, we propose a general method to address these limitations, grounding on a novel interpretation of SC as distributions over hard clusterings, which we call distributional measures. We provide an in-depth study of complexity-and metric-theoretic properties of the proposed approach, and we describe approximation techniques that can make the calculations tractable. Finally, we illustrate our approach through a simple but illustrative experiment.
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Dates et versions

hal-03830510 , version 1 (26-10-2022)

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Andrea Campagner, Davide Ciucci, Thierry Denœux. A Distributional Approach for Soft Clustering Comparison and Evaluation. 7th International Conference on Belief Functions (BELIEF 2022), Oct 2022, Paris, France. pp.3-12, ⟨10.1007/978-3-031-17801-6_1⟩. ⟨hal-03830510⟩
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