基于新相似度的模糊协同聚类改进算法
Improved Fuzzy Collaborative Clustering Algorithm Based on New Similarity

提出一种优化传统协同聚类中模糊点类别归属的改进算法，该算法引入基于清晰半径的新相似性距离公式，用超球体中心区域代替传统算法中的类中心，在各子集初始聚类结果的基础上，对容易导致类别归属错误的模糊点重新计算隶属度，得到较为清晰的聚类结果。实验结果显示，改进算法能很大程度地减少边界上的模糊点个数及纠正分类错误，清晰半径的引入还能弱化各子集之间协同系数的差异，使得参数设置更为简单。
An improved algorithm is proposed to correct the assignments of fuzzy points for the previous fuzzy collaborative clustering. The new expression of similarity distance based on clear radius is introduced, and the hypersphere central region is used to represent one cluster instead of the traditional center. In the light of initial results of separated subsets, the membership degrees are recalculated for the fuzzy points in which wrong assignments easily occurred, and finally the more clear-cut partition is obtained. The experimental results show that the improved algorithm can reduce the fuzzy points widely distributed near the boundary and correct quite part of the wrong partitions. Moreover, the method of clear radius can simplify the parameter setting by weakening the difference of collaborative coefficients.