An efficient density and grid based clustering algorithm

Clustering is one of the basic data mining tasks that can be used to help to understand the hidden information present in data sets density-based spatial clustering of applications with noise(DBSCAN),which is a typical density-based clustering algorithm,can detect arbitrary shaped clusters and handle noise well,but its computational complexity is unacceptable.In this paper,we present an efficient density and grid based clustering algorithm(DGCA)to enhance the performance of DBSCAN by partitioning data into clustering grids and merging clusters mutually.Synthetic data sets and SEQUOIA 2000 benchmark are used for experimental evaluation to study the performance theoretically.Experimental results show that the efficiency and quality for clustering of the proposed algorithm are remarkably superior to those of DBSCAN.