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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 7/2018

08.02.2017 | Original Article

A robust density peaks clustering algorithm using fuzzy neighborhood

verfasst von: Mingjing Du, Shifei Ding, Yu Xue

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 7/2018

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Abstract

The density peaks (DP) clustering approach, a novel density-based clustering algorithm, detects clusters with arbitrary shape. However, this method uses a crisp neighborhood relation to calculate local density. It cannot identify the different values of the neighborhood membership degrees of the points with respect to different distances from core point. The proposed FN-DP (fuzzy neighborhood density peaks) clustering algorithm uses fuzzy neighborhood relation to define the local density in FJP (fuzzy joint points) algorithm. The proposed algorithm integrates the speed of DP clustering algorithm with the robustness of FJP algorithm. The experimental results illustrate the superior performance of our algorithm compared with the DP clustering approach.
Vorheriger Artikel Multi-level preference regression for cold-start recommendations
Nächster Artikel The additive consistency measure of fuzzy reciprocal preference relations

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Metadaten
Titel
A robust density peaks clustering algorithm using fuzzy neighborhood
verfasst von
Mingjing Du
Shifei Ding
Yu Xue
Publikationsdatum
08.02.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 7/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0636-1

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