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 …

In this paper, we present an improved fuzzy C-means (FCM) algorithm for image
segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The …

Clustering with multiview data is becoming a hot topic in data mining, pattern recognition,
and machine learning. In order to realize an effective multiview clustering, two issues must …

Smoothing by neighborhood information is an effective way for clustering methods to
improve the robustness of image segmentation. But the usual smoothing will make some …

This paper introduces sparsity in the traditional fuzzy clustering framework and presents two
novel clustering methods. The first one is called deviation-sparse fuzzy c-means (DSFCM) …

Background: The brain magnetic resonance imaging (MRI) image segmentation method
mainly refers to the division of brain tissue, which can be divided into tissue parts such as …

Fuzzy c-means (FCM) clustering had been extended for handling multi-view data with
collaborative idea. However, these collaborative multi-view FCM treats multi-view data …

For many datasets, it is a difficult work to seek a proper cluster structure which covers the
entire feature set. To extract the important features and improve the clustering, the maximum …

In this paper, a novel unsupervised change detection method called adaptive Contourlet
fusion clustering based on adaptive Contourlet fusion and fast non-local clustering is …

Although Takagi-Sugeno-Kang (TSK) fuzzy classifier has been applied to a wide range of
practical scenarios, how to enhance its classification accuracy and interpretability …