Fuzzy clustering using the convex hull as geometrical model
type: Item
instance of: scholarly article
title: Fuzzy clustering using the convex hull as geometrical model
zbMATH Open document ID: 1468.62313
DOI: 10.1155/2015/265135
author: Luca Liparulo
author: Andrea Proietti
author: Massimo Panella
published in: Advances in Fuzzy Systems
publication date: 2018-05-18T00:00:00Z
review text: Summary: A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.
Mathematics Subject Classification ID: 62H86
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 6870096
Wikidata QID: Q59101906
describes a project that uses: Qhull
describes a project that uses: UCI-ml
describes a project that uses: DENFIS
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/265135
OpenAlex ID: W2085208219
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DBLP publication ID: journals/afs/LiparuloPP15
description: scientific article; zbMATH DE number 6870096