When feature weight parameters are introduced to the distance formula,the performance will depend on the weight values and accordingly can be improved by adj.

An improved K-means clustering algorithm is proposed,which is based on basic K-means Algorithm,and which choose original clustering centers based on densities and improves clustering effects according to feature weight learning.

Performance of feature weighting computation directly influences precision of text classification or clustering.

Based on the fundamental theory of clustering analysis and fuzzy mathematics,the numerical and attributes characteristic samples of history flood were established,and the characteristic samples were analyzed on a basin by using feature weighted fuzzy clustering algorithm.

Feature weighted is the general case of feature selection,which has better performance than(or at least has the same performance as) feature selection.

In this paper,distance metric and feature weighted algorithms have been provided for pretreatment of data which are gathered in the network.

Analysis and improvement of feature weighting method TF·IDF in text categorization;

To weaken the effects of blur edges and much noise in infrared images,a novel approach on recognition for infrared images based on Gabor filters and feature weighting is presented in this paper.

In this paper we focus our attention on the works in this field, which include four parts: an improved Gabor feature extraction algorithm based on feature weighting, infrared vehicle detection with Gabor filters and Support Vector Machines (SVM), vehicle clas.