Document clustering based on Web-log mining

The effectiveness and efficiency are two problems in clustering algorithms. DBSCAN(density based spatial clustering of applications with noise) is a typical density based clustering(RDBC) algorithm that is very efficient on large databases. A recursive density based clustering algorithm that can adaptively change its parameters intelligently is presented. This clustering algorithm RDBC is based on DBACAN. It can be shown that RDBC require the same time complexity as that of the DBSCAN algorithm. In addition, it is proved both analytically and experimentally that this method yields results more superior than that of DBSCAN.