Multi-Parameter-Setting Based on Data Original Distribution for DENCLUE Optimization

Abstract

DENCLUE is a typical density-based clustering method, and it also is an important pattern classification analysis technique. In this clustering method, the propriety of parameters’ values greatly influences the quality of distinguishing. Accordingly, how to choose an appropriate value of every parameter is a problem that is worth studying. The problem is focused in this paper, and a method which is very different from previous ones is presented. The highlight of the method is that the selection of parameters no longer depends on personal experience but on data original distribution. More specifically, smoothing parameter $h$ is more or less proportional to the average value of distance between two arbitrary data points; step size $\delta $ is adapted according to the density of data points in the hill-climbing progress; noise threshold $\xi $ is replaced by $\delta $ and total number of data points. Compared with the original DENCLUE and an improved DENCLUE proposed in our previous research, the optimized algorithm can bring better clustering from experiments. In addition, due to the adaptiveness of $\delta $ , the method will become less complex.

Keywords

• DENCLUE
• multi-parameter-setting
• optimized
• data distribution
• step size adaptive