基于核函数的模糊C均值聚类算法

Study on Fuzzy C-means Clustering Algorithm Based on Kernel Function

通过引入M ercer核,把输入空间的样本映射到高维特征空间,实现了对样本在特征空间的优化,使各类样本之间的差别增大,从而较好地实现了对差别微弱的样本类之间的聚类.仿真实验的结果证实了该方法的可行性和有效性.

By using Mercer kernel functions, the data in the original space are mapped to a high-dimensional feature space. The data are optimized in feature space and thus the difference among data is expanded. Performing FCM clustering algorithm in feature space, the data whose difference is small can be clustered accurately. The results of simulation experiments show the feasibility and effectiveness of the fuzzy kernel Cmeans clustering algorithm.