基于L-ISOMAP降维的快速模糊聚类算法

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。

Fast fuzzy clustering algorithm based on L-ISOMAP for dimensional reduction

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 dimension-ality＂.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 da-ta,uses landmark isometric（L-ISOMAP） algorithm to reduce the dimension,further analyzes on the basis,obtains the final re-sults.Experimental results show that the effectiveness and real-time of FCM-LI algorithm in high dimensional feature analysis.