Unsupervised Multiview Fuzzy C-Means Clustering Algorithm

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.

Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements

“…It is true that the applications of neural networks for dimensionality reduction have been explored in other fields such as hydrology [29] or medical imaging processing [30]. Other unsupervised strategies for dimensionality reduction includes multiview fuzzy c-means clustering algorithms, a technique used in data analysis and machine learning to group data points into clusters when multiple perspectives or views of data are available [31]. The main advantage of ANN in land classification problems is its contribution to solving the local minima problem present in traditional clustering algorithms, as well as being able to separate complex populations.…”