关键词: 模糊C-均值聚类, 等距映射, 非线性降维

Abstract: Fuzzy C-means（FCM） clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition.However，FCM algorithm in the iterative process requires a lot of calculations，especially when feature vectors has high-dimensional，using clustering algorithm to sub-heap，not only is inefficient，but also may lead to “the curse of dimensionality”.For the problem，this paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process，the problem of cluster center is an np-hard problem.In order to improve the effectiveness and real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis，an improved algorithm FCM-LI is proposed combining of landmark isometric（L-ISOMAP） algorithm.It analyzes the samples preliminarily，uses clustering results and the correlation of sample data，uses landmark isometric（L-ISOMAP） algorithm to reduce the dimension，further analyzes on the basis，obtains the final results.Experimental results show that the effectiveness and real-time of FCM-LI algorithm in high dimensional feature analysis.

Key words: fuzzy C-means clustering, isometric feature mapping, nonlinear dimensionality reduction