This paper proposes a fast colore image segmentation algorithm based on wavelet transform and Kernelized Fuzzy C-Mean(KFCM) clustering algorithm.

The analog circuits fault diagnosis based on fuzzy C-means clustering;

Segmentation of fat and lean meat in beef images based on fuzzy C-means clustering;

Brain MR image segmentation based on anisotropic Gibbs random field and fuzzy C-means clustering model;

Escaped toll analysis of ETC system customer data based on fuzzy C-means clustering;

In order to recognize the pollutant sources and build the correspondence relationships between contaminated sources and important pollutants,a set of intelligent recognizing method based on correspondence factor analysis and fuzzy C-means clustering(short for IRM-CFA&FCM) is developed.

To improve the accuracy of text clustering,fuzzy c-means clustering based on topic concept sub-space(TCS2FCM) is introduced for classifying texts.

A novel algorithm for discretization of continuous attributes in rough set theory based on FCM;

Based on the idea of combining models to improve prediction accuracy and robustness, the soft sensor model of the dry point of the first top naphtha is built by using FCM to divide a whole training dataset into several clusters with different centers.

With only pixel value information taken into account and non-robust Euclidean distance used as the distance measure standard, the classical Fuzzy C-means Clustering (FCM) algorithm lacks enough robustness in the image segmentation.