Unsupervised Multiview Fuzzy C-Means Clustering Algorithm

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 fo...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Electronics (Basel) Ročník 12; číslo 21; s. 4467
Hlavní autoři: Hussain, Ishtiaq, Sinaga, Kristina P., Yang, Miin-Shen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2023
Témata:
Algorithms
Analysis
Augmented reality
Clustering
Clustering (Computers)
Collaboration
Datasets
Electronic data processing
Fuzzy sets
Machine learning
Memberships
Methods
Social networks
Unsupervised learning
Taiwan
ISSN:2079-9292, 2079-9292
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12214467