关键词: 变密度聚类算法, 基于密度的聚类, DBSCAN, 数据挖掘

Abstract: Density clustering has been widely used with such advantages as：its clusters are easy to understand and it does not limit itself to shapes of clusters.But existing density-based algorithms have trouble in finding out all the meaningful clusters for datasets with varied densities.This paper introduces a new algorithm called VDBSCAN for the purpose of varied-density datasets analysis.The basic idea of VDBSCAN is that，before adopting traditional DBSCAN algorithm，k-dist plot and DK（Difference between k-dists of neighboring points） analysis are used to select several values of parameter Eps for different densities.With different values of Eps，it is possible to find out clusters with varied densities simultaneity.Finally，4 synthetic 2-dimension databases are used for demonstration，and experiments show that VDBSCAN is efficient in successfully clustering uneven datasets.

Key words: Varied Density Based Clustering Algorithm（VDBSCAN）, density-based clustering, Density Based Spatial Clustering of Application with Nose（DBSCAN）, data mining