The model is trained by the mea-sured parameters of die-casting machine,adopting fuzzy K means clustering algorithm to select clustering centre,using the observer to forecast the residuals between the sensor output value and the sensor actual output value to diagnose sensor fault.

For getting quickly the color image,hue-saturation-intensity(HSI) color space is used in which an enhanced fuzzy K means clustering method is designed.

Data mining model based on improved fuzzy k-means algorithm and neural network algorithm;

Data recorded with a camera network is clustered hierarchically using fuzzy K-means algorithm based on spatial and temporal information respectively,and the quality of each clustering results is evaluated by the tightness and separation criterion(TSC).

The learning of this method is divided into two processes,state space learning using K-means clustering algorithm for adaptive discretization of continuous states and policy learning using Sarsa algorithm for finding optimal policy.

Application of the improved K-means clustering algorithm in support vector machine;

According to the characters of the images, the algorithm separated image into several regions by K-means clustering algorithm, and each region is equalized respectively within their gray levels.