Research of clustering algorithm based on density gradient
基于密度梯度的聚类算法研究

In order to solve difficult problems in clustering with irregularly distributed data set, a new clustering algorithm based on density gradient was provided. By analyzing the changing density of data sample and its neighbors, the algorithm searched points with the maximum density and took them as original centers of clusters. Then it combined some smaller clusters into larger ones according to the distribution of border points between clusters. Experimental results show that the new algorithm has better performance than Density Based Spatial Clustering of Applications with Noise(DBSCAN).