Separating a given data set into groups (clusters) based on their natural similar
characteristics is one of the main concerns in data clustering. A cluster can be defined as a …

Fuzzy c-means (FCM) is one of the most frequently used methods for clustering. However,
with increasing amount of data, FCM suffers from slow convergence and a large amount of …

This paper proposes a gradient-based data fusion and classification approach for Synthetic
Aperture Radar (SAR) and optical image. This method is used to intuitively reflect the …

The objective of this research is to enhance performance of Stochastic Gradient Descent
(SGD) algorithm in text classification. In our research, we proposed using SGD learning with …

Clustering documents into coherent categories is a very useful and important step for
document processing and understanding. The introducing of fuzzy set theory into clustering …

Image clustering is a key technique for better accomplishing image annotation and
searching in large image repositories. Fuzzy c-means and its variations have achieved …

Image segmentation with clustering approach is widely used in biomedical application.
Accurate brain Magnetic Resonance (MR) image segmentation is a challenging task due to …

Enhancing the effectiveness of clustering methods has always been of great interest.
Therefore, inspired by the success story of the gradient descent approach in supervised …

The fuzzy C-means (FCM) algorithm is a classical clustering algorithm which is widely used.
However, especially for high-dimensional data sets with complex structures, the large-scale …

Nonnegative matrix factorization (NMF) is a very effective method for high dimensional data
analysis, which has been widely used in computer vision. However, the conventional NMF is …