Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
ACM SIGKDD Explorations Newsletter
A fine grained heuristic to capture web navigation patterns
ACM SIGKDD Explorations Newsletter
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Interactive path analysis of web site traffic
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining web logs for prediction models in WWW caching and prefetching
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering for opportunistic communication
Proceedings of the 11th international conference on World Wide Web
Clustering the Users of Large Web Sites into Communities
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Unified Framework for Clustering Heterogeneous Web Objects
WISE '02 Proceedings of the 3rd International Conference on Web Information Systems Engineering
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Discovery of User Communities from Web Audience Measurement Data
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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The Web is a huge network composed of Web pages and hyperlinks. It is often reported that related Web pages are densely linked with each other. Finding groups of such related pages, which are called Web communities, is important for information retrieval from the Web. Several attempts have been made for the discovery of Web communities such as Kumar's trawling and Flake's method. In addition to the communities of related Web pages, there are communities of users sharing common interests. Finding the latter communities, which we called user communities in this paper, is also important for clarifying the behaviors of Web users. It is expected that the characteristics of user communities in the Web correspond to those in real human communities. A method for discovering user communities is described in this paper. Client-level log data (Web audience measurement data) is used as the data of users' Web watching behaviors. Maximal complete bipartite graphs are searched from term-user graph obtained from the log data without analyzing the contents of Web pages. Experimental results show that our method succeeds in discovering many interesting user communities with labels that characterize the communities.