Improved Fuzzy Clustering Algorithm Based on Data Weighted Approach
基于数据加权策略的模糊聚类改进算法

A new data exponent weighted fuzzy clustering approach is proposed by introducing a set of exponent weighting factors and influence exponent, the new approach makes it possible to treat the data points discriminatively. The new approach is combined with the existing Gustafson-Kessel (G-K) algorithm and a new algorithm, DWG-K is presented. Numerical experiments show that the DWG-K is better than G-K in improving the quality of clustering, and in the outliers mining, DWG-K detects the outliers with the global view and the physical meaning of outliers is clearer, and moreover, the computational efficiency is significantly higher than the current widely used density-based method.