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Pré-Publication, Document De Travail Année : 2022

Assessing clustering methods using Shannon's entropy

Résumé

Unsupervised clustering algorithms are a very important source of information for how a dataset may be classified into subgroups of homogeneous sets. For these algorithms we present a full analysis of their quality by introducing a clustering confidentness metric. Based on this metric, a statistical test and a correction of cluster probabilities are introduced to improve the performance of the different algorithms. These results are illustrated by simulation analysis and by an application on a real world data set.
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Dates et versions

hal-03812055 , version 1 (12-10-2022)
hal-03812055 , version 2 (09-11-2023)

Identifiants

  • HAL Id : hal-03812055 , version 1

Citer

Anis Hoayek, Didier Rullière. Assessing clustering methods using Shannon's entropy. 2022. ⟨hal-03812055v1⟩
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