Kernel K-means clustering algorithm based on local density
一种基于局部密度的核K-means算法*

In order to solve the problem that original clustering centers of kernel K-means algorithm is difficult to determine,proposed a kernel K-means clustering algorithm based on local density(LDKK).This algorithm applied local relative density of each data to choose the points with high density and low similarity as the initial cluster centers. Experimental results show that the algorithm can eliminate the impact of edge points and noise points, and adapt to the imbalance of each actual type of data set in the density distribution, which can eventually generate higher quality and less volatility clustering.