Collaborative filtering (CF) based recommender systems are typically trained based on
personal interaction data (eg, clicks and purchases) that could be naturally represented as …

This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image
clustering. The proposed algorithm incorporates the local spatial information and gray level …

Fuzzy c-means clustering (FCM) with spatial constraints (FCM/spl I. bar/S) is an effective
algorithm suitable for image segmentation. Its effectiveness contributes not only to the …

Fuzzy c-means (FCM) algorithms with spatial constraints (FCM_S) have been proven
effective for image segmentation. However, they still have the following disadvantages:(1) …

Nondestructive imaging methods such as X‐ray computed tomography (CT) yield high‐
resolution, three‐dimensional representations of pore space and fluid distribution within …

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to
spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to …

Image segmentation plays a crucial role in many medical imaging applications. In this paper,
we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) …

Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system; neural networks provide a model …

This paper presents a survey of latest image segmentation techniques using fuzzy
clustering. Fuzzy C-Means (FCM) Clustering is the most wide spread clustering approach for …