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

摘要： 模糊C-均值聚类算法是非监督模式识别中广泛应用的算法之一。但是，FCM算法在迭代过程中需要大量的计算，尤其当特征向量维数较高时，使用聚类分堆训练，不仅效率低下，还有可能导致“维数灾难”。针对该问题，分析模糊C-均值聚类算法在高维特征分析过程中，聚类中心的求解问题是一个np-hard问题，为了提高模糊C-均值聚类算法在高维特征分析中的实时性与有效性，结合界标等距映射（L-ISOMAP）算法，提出了改进算法FCM-LI，先对样本初步分析，利用聚类结果及样本数据相关性，使用界标等距映射（L-ISOMAP）算法降维，在此基础上进一步分析，获得最终分析结果。通过实验证明，FCM-LI算法在高维数据分析过程中的有效性与实时性。

关键词: 模糊C-均值聚类, 等距映射, 非线性降维