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Conference Papers Year : 2022

Fuzzy and Evidential Contribution to Multilevel Clustering

Abstract

Clustering algorithms based on split-and-merge concept, divisive or agglomerative process are widely developed to extract patterns with different shapes, sizes and densities. Here a multilevel approach is considered in order to characterise general patterns up to finer shapes. This paper focus on the contribution of both fuzzy and evidential models to build a relevant divisive clustering. Algorithms and both a priori and a posteriori split criteria are discussed and evaluated. Basic crisp/fuzzy/evidential algorithms are compared to cluster four datasets within a multilevel approach. Finally, same framework is also applied in embedded spectral space in order to give an overall comparison.

Dates and versions

hal-03909904 , version 1 (21-12-2022)

Identifiers

Cite

Martin Cabotte, Pierre-Alexandre Hébert, Émilie Poisson-Caillault. Fuzzy and Evidential Contribution to Multilevel Clustering. 14th International Conference on Fuzzy Computation Theory and Applications, Oct 2022, Valletta, Malta. pp.217-224, ⟨10.5220/0011550800003332⟩. ⟨hal-03909904⟩
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