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- W4283325234 abstract "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 emph{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." @default.
- W4283325234 created "2022-06-24" @default.
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- W4283325234 date "2022-06-20" @default.
- W4283325234 modified "2023-09-27" @default.
- W4283325234 title "A Distributional Approach for Soft Clustering Comparison and Evaluation" @default.
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- W4283325234 doi "https://doi.org/10.48550/arxiv.2206.09827" @default.
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