Recursive-Partitioned DBSCAN

DBSCAN, which is the one of the density-based clustering methods in data mining, does the process of clustering, according to density of data. Although DBSCAN method seems effective in the small data sets, its efficiency in terms of processing time decreases with the growing of data volumes. Because of this reason, DBSCAN as a clustering method is not considered a suitable clustering method for large data sets. For this reason, R-P-DBSCAN (Recursive-Partitioned DBSCAN) algorithm is proposed. The new algorithm is based on partitioning & combining and DBSCAN algorithm is used for data clustering. Large-volume data sets are divided into smaller pieces and clustered by DBSCAN. Then, combining each clustered piece, until whole set of data is clustered. Each cluster obtained by R-P-DBSCAN, is the same as the clusters obtained with the classical DBSCAN. The results obtained with R-P-DBSCAN have shown that, the proposed algorithm has better clustering performance (until 85%) according to classical DBSCAN algorithm.