基于概率典型性和聚类排斥的无噪声模糊聚类方法

提出了建立在概率典型性和聚类排斥基础上的一个新型无噪声模糊聚类方法RTCM，给出了它的迭代算法过程，并验证了它的收敛性．首先引述了一般的聚类方法，它们主要分为两种：噪声聚类，如模糊c均值（FCM）、可能模糊c均值（FPCM）；无噪声聚类，如NC、PCM等，然后给出了RTCM算法模型和过程，并验证了它的局部收敛性．该算法解决噪声环境下的数据聚类问题，避免了重叠聚类．对比试验表明，该算法改善了噪声环境下FCM，NC、PCM、FPCM的聚类中心质量，有效地解决了PCM在近邻聚类数据中的聚类重叠问题．

New Noise-Resistant Fuzzy Clustering Approach Based on Probabilistic Typicalities and Cluster Repulsion

This paper presents a new noise-resistant fuzzy clustering approach based on probabilistic typicalities and cluster repulsion, denoted as RTCM, proposes a new iteration algorithm,furthermore, validates its converge. Firstly, some existed fuzzy clustering methods are introduced, which commonly include two types: noise clustering, such as FCM, FPCM, and noise-resistant fuzzy clustering, such as NC, PCM, and then the objective function and iteration algorithm are proposed, the proof is given for its converge properties. Experimental results show that our algorithm improves the quality of clustering centers of data sets including noise comparing to FCM, NC, PCM, FPCM, solves the coincident clusters in PCM.