BIRCHSCAN: A sampling method for applying DBSCAN to large datasets

Abstract

The DBSCAN algorithm is a traditional density-based clustering method. This algorithm allows to identify clusters of different shapes, with the ability to manage noisy patterns in the data. DBSCAN usually presents good results, however it performs several distance calculations in the clustering process. This leads to a low efficiency and its application in large datasets is not recommended. This work presents a new method to apply DBSCAN to a reduced set of elements in order to cluster the entire dataset. Therefore, the method allows clustering large datasets with similar results to the outcoe of DBSCAN on the entire dataset. Experiments results suggest that the proposed technique presents good results and consistency compared to other algorithms with similar approach.