The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
HyPursuit: a hierarchical network search engine that exploits content-link hypertext clustering
Proceedings of the the seventh ACM conference on Hypertext
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
ACM Computing Surveys (CSUR)
Introduction to Algorithms
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Link mining: a new data mining challenge
ACM SIGKDD Explorations Newsletter
Editorial: special issue on web content mining
ACM SIGKDD Explorations Newsletter
Hyperlink analysis on the world wide web
Proceedings of the sixteenth ACM conference on Hypertext and hypermedia
DBRS: a density-based spatial clustering method with random sampling
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Web document clustering using hyperlink structures
Computational Statistics & Data Analysis
Density link-based methods for clustering web pages
Decision Support Systems
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So far several methods have been proposed for clustering the web. On the other hand, many algorithms have been developed for clustering the relational data, but their usage for the Web is to be investigated. One main category of such algorithms is density based methods providing high quality results. In this paper first, a new density based algorithm is introduced and then it is compared with other algorithms of this category. The proposed algorithm has some interesting properties and capabilities such as hierarchical clustering and sampling, making it suitable for clustering the web data.