An adaptive density-based clustering algorithm for spatial database with noise

Clustering spatial data has various applications. Several clustering algorithms have been proposed to cluster objects in spatial databases. Spatial object distribution has significant effect on the results of clustering. Few of current algorithms consider the distribution of objects while processing clusters. In this paper we propose an adaptive density-based clustering algorithm, ADBC, which uses a novel adaptive strategy for neighbor selection based on spatial object distribution to improve clustering accuracy. We perform a series of experiments on simulated data sets and real data sets. A comparison with DBSCAN and OPTICS shows the superiority of our new approach.