关键词: 模糊聚类, w-距离均值, 初始聚类中心, 调衡因子, 抗噪性

Abstract: In this paper, a fuzzy clustering algorithm based on w-mean distance was proposed to solve such defects of Fuzzy C-Means (FCM) algorithm as easily falling into local optimal value and being sensitive to clustering center and noise data. First, initial clustering centers were determined by making use of the idea of the mean distance according to the distribution of data set, and the regulating factor w was introduced to adjust the mean distance. Second, each sample in data set was assigned a weight, and the clustering center formula and target function formula were modified by the weight, so that the anti-noise performance was greatly improved for the algorithm. In the end, the experimental results validate that the proposed algorithm has good effects on selecting initial clustering centers, avoiding local convergence, and having higher performance of anti-noise and effectiveness.

Key words: fuzzy clustering, w-mean distance, initial clustering center, regulating factor, anti-noise