Clustering algorithms aim at finding dense regions of data based on similarities and
dissimilarities of data points. Noise and outliers contribute to the computational procedure of …

Fuzzy sets emerged in 1965 in a paper by Lotfi Zadeh. In 1969 Ruspini published a seminal
paper that has become the basis of most fuzzy clustering algorithms. His ideas established …

Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

Traditional fuzzy clustering algorithms suffer from two problems in image segmentations.
One is that these algorithms are sensitive to outliers due to the non-sparsity of fuzzy …

Krishnapuram and Keller first proposed possibilistic c-means (PCM) clustering in 1993.
Afterward, PCM was widely studied with various extensions. The PCM algorithm and its …

The fuzzy c-means clustering algorithm is the most widely used soft clustering algorithm. In
contrast to hard clustering, the cluster membership of data generated using the fuzzy c …

Since social media, virtual communities and networks rapidly grow, multiview data become
more popular. In general, multiview data always contain different feature components in …

Graph-based clustering approaches, especially the family of spectral clustering, have been
widely used in machine learning areas. The alternatives usually engage a similarity matrix …

The management of uncertain information in a data set is crucial for clustering models. In
this study, we present a rough possibilistic C-means clustering approach based on …

The main objective of Fuzzy C-means (FCM) algorithm is to group data into some clusters
based on their similarities and dissimilarities. However, noise and outliers affect the …