Fuzzy and Evidential Contribution to Multilevel Clustering

Martin Cabotte, Pierre-Alexandre Hébert, Émilie Poisson-Caillault

2022

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.

Paper Citation

in Harvard Style

Cabotte M., Hébert P. and Poisson-Caillault É. (2022). Fuzzy and Evidential Contribution to Multilevel Clustering. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: FCTA; ISBN 978-989-758-611-8, SciTePress, pages 217-224. DOI: 10.5220/0011550800003332

in Bibtex Style

@conference{fcta22,
author={Martin Cabotte and Pierre-Alexandre Hébert and Émilie Poisson-Caillault},
title={Fuzzy and Evidential Contribution to Multilevel Clustering},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: FCTA},
year={2022},
pages={217-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011550800003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: FCTA
TI - Fuzzy and Evidential Contribution to Multilevel Clustering
SN - 978-989-758-611-8
AU - Cabotte M.
AU - Hébert P.
AU - Poisson-Caillault É.
PY - 2022
SP - 217
EP - 224
DO - 10.5220/0011550800003332
PB - SciTePress