VDBSCAN:varied density based clustering algorithm
Peng Liu · Computer Engineering and Applications Journal · 2009
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 be-tween k-dists of neighboring points) analysis are used to select several values of parameter Eps for different densities.With dif-ferent 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.