Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Retrieving and organizing web pages by “information unit”
Proceedings of the 10th international conference on World Wide Web
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Topic-conditioned novelty detection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Implicit link analysis for small web search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Retrieval and novelty detection at the sentence level
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Web unit mining: finding and classifying subgraphs of web pages
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A probabilistic model for retrospective news event detection
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
WAM-Miner: in the search of web access motifs from historical web log data
Proceedings of the 14th ACM international conference on Information and knowledge management
Event-Driven document selection for terrorism information extraction
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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The web is a sensor of the real world. Often, content of web pages correspond to real world objects or events whereas the web usage data reflect users' opinions and actions to the corresponding events. Moreover, the evolution patterns of the web usage data may reflect the evolution of the corresponding events over time. In this paper, we present two variants of iWed(Integrated Web Event Detector) algorithm to extract events from website data by integrating author-centric data and visitor-centric data. We model the website related data as a multigraph, where each vertex represents a web page and each edge represents the relationship between the connected web pages in terms of structure, semantic, and/or usage pattern. Then, the problem of event detection is to extract strongly connected subgraphs from the multigraph to represent real world events. We solve this problem by adopting the normalized graph cut algorithm. Experiments show that the usage patterns play an important role in iWed algorithms and can produce high quality results.