A Novel Fuzzy C-means Clustering Algorithm Based on Local Density - Intelligent Information Processing X Access content directly
Conference Papers Year : 2020

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


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
Origin : Files produced by the author(s)

Dates and versions

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





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 View
65 Download



Gmail Facebook X LinkedIn More