修正核函数模糊聚类算法

应用核函数度量的紧致性和分离性，给出了一种新的聚类有效性指标KKW，由KKW指标得到最优聚类数并用于修正核函数模糊聚类算法(MKFCM)，由于经过了修正核函数的映射，使原来没有显现的特征突显出来。用MKFCM对Wine和glass数据集进行聚类，每一类的聚类正确度大于90%；对于缺失数据的Wisconsin Breast Cancer 数据，错分率为4.72%。该聚类方法在性能上比经典聚类算法有所改进，具有更快的收敛速度以及较高的准确度。仿真实验的结果证实了修正核聚类方法的可行性和有效性。

Fuzzy clustering algorithm with modified kernel functions

Using kernelized metric of compactness and separation, this paper proposed a new clustering validity index named KKW, and obtained the optimized cluster number. Besides, the KKW index was used in the modified kernel fuzzy clustering (MKFCM) algorithm. As mapped by modified Mercer kernel functions, the data set shows new features never showed before. MKFCM algorithm was applied to the data set Wine and glass. For every clustered class, MKFCM has overall accuracy higher than 90%;as to the incomplete data set Wisconsin Breast Cancer, difference is 4.72%. The modified kernel clustering algorithm is faster than the classical algorithm in convergence and more accurate in clustering. The results of simulation experiments show the feasibility and effectiveness of the modified kernel clustering algorithm.