A Novel Fuzzy C-means Clustering Algorithm Based on Local Density - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

A Novel Fuzzy C-means Clustering Algorithm Based on Local Density

Résumé

Fuzzy C-means (FCM) clustering algorithm is a fuzzy clustering algorithm based on objective function. FCM is the most perfect and widely used algorithm in the theory of fuzzy clustering. However, in the process of clustering, FCM algorithm needs to randomly select the initial cluster center. It is easy to generate problems such as multiple clustering iterations, low convergence speed and unstable clustering. In order to solve the above problems, a novel fuzzy C-means clustering algorithm based on local density is proposed in this paper. Firstly, we calculate the local density of all sample points. Then we select the sample points with the local maximum density as the initial cluster center at each iteration. Finally, the selected initial cluster center are combined with the traditional FCM clustering algorithm to achieve clustering. This method improved the selection of the initial cluster center. The comparative experiment shows that the improved FCM algorithm reduces the number of iterations and improves the convergence speed.
Fichier principal
Vignette du fichier
498234_1_En_5_Chapter.pdf (702.05 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03456979 , version 1 (30-11-2021)

Licence

Paternité

Identifiants

Citer

Jian-Jun Liu, Jian-Cong Fan. A Novel Fuzzy C-means Clustering Algorithm Based on Local Density. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.46-58, ⟨10.1007/978-3-030-46931-3_5⟩. ⟨hal-03456979⟩
35 Consultations
61 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More