Parameter Reduction for Density-based Clustering on Large Data Sets

摘要

Clustering on large datasets has become one of the most intensively studied areas with increasing data volumes. One of the problems of clustering on large datasets is minimal domain knowledge to determine the input parameters. In the density based clustering, the main input is the minimum neighborhood radius. The problem becomes more difficult when the clusters are in different densities. In this paper, we explore an automatic approach to determine the minimum neighborhood radius based on the distribution of datasets. The algorithm, MINR, is developed to determine the minimum neighborhood radii for different density clusters based on many experiments and observations. MINR can be used together with any density based clustering method to make a nonparametric clustering algorithm. In this paper, we combine MINR with the enhanced DBCSCAN, e-DBCSCAN. Experiments show our approach, is more efficient and scalable than TURN~*.