计算机工程与应用2016，Vol.52Issue(19)：19-24,30,7.DOI:10.3778/j.issn.1002-8331.1601-0312
K-近邻估计协同系数的协同模糊C均值算法
Novel collaboration fuzzy C-means algorithm with K-nearest neighbor method determined Collaboration Coefficient
赵慧珍 1刘付显 1李龙跃1
作者信息
- 1. 空军工程大学 防空反导学院，西安 710051
- 折叠
摘要
Abstract
The collaboration coefficient of Collaboration Fuzzy C-Means(CFC)algorithm is always determined by priori knowledge and remains constant during collaboration stages, with an inadequate using of the collaborative relationship. In order to circumvent this limitation, a novel CFC algorithm with K-nearest neighbor method determined collaboration coef-ficient is developed. Firstly, fuzzy partition matrix and cluster prototypes of every sub data sets are computed by Fuzzy C-Means(FCM)algorithm. Secondly, the number of nearest neighbors is setting and density of the cluster prototypes is gained by K-nearest neighbor method, forming density matrix. Thirdly, it dynamically adjusts collaborative coefficient by the correlation of density matrix. Lastly, it clusters objects with dynamical collaborative coefficient. Examples are provided to demonstrate the rationality of collaboration coefficient and the performance of collaboration FCM algorithm.关键词
K-近邻/密度/模糊C均值/协同系数Key words
K-nearest neighbor/density/Fuzzy C-Means algorithm/collaborative coefficient分类
信息技术与安全科学引用本文复制引用
赵慧珍,刘付显,李龙跃..K-近邻估计协同系数的协同模糊C均值算法[J].计算机工程与应用,2016,52(19):19-24,30,7.
