计算机应用研究2012，Vol.29Issue(8)：2849-2851,3.DOI:10.3969/j.issn.1001-3695.2012.08.012
基于自适应权重的模糊C-均值聚类算法
Fuzzy C-means clustering based on self-adaptive weight
摘要
Abstract
Due to fuzzy C-means clustering algorithm rely heavily on randomly select C clustering centers,so outlier and uneven distribution of the samples easily influenced and made it easy to fall into the local optimum states. Therefore, this paper proposed an improved fuzzy C-means clustering algorithm based on self-adaptive weights. The new method expressed weight by using the Gaussian distance ratio,it computed the weights for every data according to the current clustering state and no more did rely on the initial clustering center, weakened the influence of outlier and uneven distribution of the samples. The experiments indicate that the fuzzy C-means clustering algorithm based on self-adaptive weights is an effective fuzzy clustering algorithm, has more robust and higher clustering accuracy.关键词
模糊C-均值聚类算法/自适应权重/高斯距离/隶属矩阵Key words
fuzzy C-means clustering algorithm/self-adaptive weights/Gaussian distance/membership matrix分类
信息技术与安全科学引用本文复制引用
任丽娜,秦永彬,许道云..基于自适应权重的模糊C-均值聚类算法[J].计算机应用研究,2012,29(8):2849-2851,3.基金项目
国家自然科学基金资助项目(60863005) （60863005）
贵州省科学技术基金资助项目(黔科台J字[2012]2125号) （黔科台J字[2012]2125号）
贵州大学引进人才科研资助项目(贵大人基合字[2011]14号) （贵大人基合字[2011]14号）
