基于D-S证据理论的模糊聚类集成

针对现有集成方法在处理模糊聚类时的不足，提出一种新的模糊聚类集成方法。在证据合成的理论框架内，讨论在识别框架、概率分配函数、合成规则等问题。给出了3种基本概率分配方法：近似类别分配概率方法、归一化模糊海明距离方法以及二者证据合成的方法。分析指出合成的方法能够较好利用二者的优势进行互补，获取更为合理的基本概率分配方法。最后，通过实验讨论所提方法的参数设置、收敛性和有效性等问题。

Fuzzy clustering ensemble based on D-S theory

In order to overcome the weakness of present ensemble methods for fuzzy clustering, a novel method of fuzzy clustering ensemble is proposed. In the framework of evidence ensemble, the recognition framework, basic probability assignment and synthesis rules are analyzed. Three different basic probability assignment (BPA) methods is proposed, which are approximation probability of clusters, normalized fuzzy Hamming distance, and evidence synthesis of the two. Because of complementary advantages of the two methods, the third method is able to get more useful BPA. At last, parameter settings, convergence and effectiveness of the method proposed are analyzed by experiment.