Document Clustering Based on Web-Log Mining
基于Web-Log Mining的Web文档聚类

The effectiveness and efficiency are two problems in clustering algorithms. DBSCAN is a typical density based clustering algorithm that is very efficient on large databases. In this paper, a recursive density based clustering algorithm that can adaptively change its parameters intelligently is presented. This clustering algorithm RDBC (recursive density based clustering algorithm) is based on DBSCAN. 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.