关键词: 密度聚类, DBSCAN算法, 自适应, 核密度估计, 参数寻优

Abstract:

Aiming at the problem that traditional DBSCAN algorithm needs to input [Eps] and [MinPts] parameters manually, and improper parameter selection leads to low clustering accuracy, an improved adaptive parameter density clustering algorithm is proposed. Firstly, the kernel density estimation is used to determine the reasonable interval of [Eps] and [MinPts] parameters, and the cluster number is determined by analyzing the local density characteristics of the data. Then, the clustering is performed according to the parameter values within the reasonable interval. Finally, the contour coefficients satisfying the cluster number condition are calculated, and the parameter corresponding to the maximum contour coefficient is the optimal parameter. The comparison experiments on four classical datasets show that the algorithm can automatically select the optimal [Eps] and [MinPts] parameters, and the accuracy is improved by 6.1% on average.

Key words: density clustering, DBSCAN algorithm, self-adaptive, kernel density estimation, parameter optimization