动态加权模糊核聚类算法

为了克服噪声特征向量对聚类的影响,充分考虑各特征向量对聚类结果的贡献度的不同,运用mercer核将待聚类的数据映射到高维空间,提出了一种新的动态加权模糊核聚类算法.该算法运用动态加权,自动消弱噪声特征向量在分类中的作用,在对数据没有任何先验信息的情况下,不仅能够准确划分线性数据,而且能够做到非线性划分非团状数据.仿真和实际数据分类结果表明,数据中的噪声对分类结果影响较小,该算法具有很高的实用性.

Fuzzy clustering algorithm with dynamic weights based on kernel method

In order to overcome the influence of noise feature vector on clustering, take full account of the different contribution of feature vector on clustering, a new fuzzy clustering algorithm with dynamic weights based on the kernel method is presented. The algorithm cluster data with noise features in high feature space mapped by the mercer kernel, not only can overcome the influence of noise feature vector on clustering, but also cluster the line and the non-group data without any experience, the clustered results of simulation data and real data show that this new algorithm may be used as a more accurate method in clustering data.