关键词: 模糊C-均值, 权值, 聚类

Abstract: The fuzzy C-means clustering algorithm has the shortages including its sensitivity for data of outlier and noise and its distributed imbalanced samples，this paper presents an improved algorithm：By improving the subject function，the impacts of outlier are eliminated，and in order to differentiate the different effects of different samples for knowledge discovery，every sample holds a quantificational weight to improve clustering results of noise and distributed imbalanced samples.The experimental results show that the modified algorithm is more robust and has higher clustering accuracy.

Key words: Fuzzy C-Means（FCM）, weights, clustering