Back to Search
Start Over
Unsupervised Multiview Fuzzy C-Means Clustering Algorithm.
- Source :
- Electronics (2079-9292); Nov2023, Vol. 12 Issue 21, p4467, 30p
- Publication Year :
- 2023
-
Abstract
- The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended to process multiview datasets in the literature, called the multiview FCM (MV-FCM). However, most of the MV-FCM clustering algorithms and their extensions in the literature need prior information about the number of clusters and are also highly influenced by initializations. In this paper, we propose a novel MV-FCM clustering algorithm with an unsupervised learning framework, called the unsupervised MV-FCM (U-MV-FCM), such that it can search an optimal number of clusters during the iteration process of the algorithm without giving the number of clusters a priori. It is also free of initializations and parameter selection. We then use three synthetic and six benchmark datasets to make comparisons between the proposed U-MV-FCM and other existing algorithms and to highlight its practical implications. The experimental results show that our proposed U-MV-FCM algorithm is superior and more useful for clustering multiview datasets. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20799292
- Volume :
- 12
- Issue :
- 21
- Database :
- Complementary Index
- Journal :
- Electronics (2079-9292)
- Publication Type :
- Academic Journal
- Accession number :
- 173568415
- Full Text :
- https://doi.org/10.3390/electronics12214467