关键词: 一种经典的基于密度的聚类算法（DBSCAN）, 核密度估计, 自适应, 聚类

Abstract: DBSCAN algorithm needs Eps and minPts two parameters, leading to the accuracy of clustering results directly depends on the user’s choice of parameters, thus this paper puts forward a new method of parameter determination. It adopts nonparametric kernel density estimation theory to analyse the distribution features of the data samples to automatically determine the Eps and minPts parameters, avoiding the manual intervention of clustering process, and achieving automation of clustering process. Theoretical analysis and experimental results show that this method is able to choose reasonable parameters of Eps and minPts and clustering results with higher accuracy are obtained.

Key words: Density Based Spatial Clustering of Applications with Noise（DBSCAN）, kernel density estimation, self-adaptive, clustering