As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is
often introduced to an objective function to improve the robustness of the FCM algorithm for …

A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely
used for grayscale and color image segmentation. However, most of them are time …

Membership regularized fuzzy clustering methods apply an important prior that neighboring
data points should possess similar memberships according to an affinity/similarity matrix. As …

Clustering algorithms by minimizing an objective function share a clear drawback of having
to set the number of clusters manually. Although density peak clustering is able to find the …

The fuzzy C-means (FCM) clustering procedure is an unsupervised form of grou** the
homogenous pixels of an image in the feature space into clusters. A brain magnetic …

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) …

In this paper, we elaborate on residual-driven Fuzzy C-Means (FCM) for image
segmentation, which is the first approach that realizes accurate residual (noise/outliers) …

Abstract The Fuzzy C-means (FCM) clustering algorithm is an effective method for image
segmentation. Non-local spatial information considers more redundant information of the …

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 …

Mobility patterns at region level can provide more macroscopic and intuitive knowledge on
how people gather in or depart from the region. However, the analysis and prediction of …