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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. [ABSTRACT FROM AUTHOR]

Existing web usage mining techniques focus only on discovering knowledge based on the statistical measures obtained from the static characteristics of web usage data. They do not consider the dynamic nature of web usage data. In this paper, we present an algorithm called Cleopatra (CLustering of EvOlutionary PAtTeRn-based web Access sequences) to cluster web access sequences ( ) based on their evolutionary patterns. In this approach, Web access sequences that have similar change patterns in their support counts in the history are grouped into the same cluster. The intuition is that often are event/task-driven. As a result, related to the same event/task are expected to be accessed in similar ways over time. Such clusters are useful for several applications such as intelligent web site maintenance and personalized web services. [ABSTRACT FROM AUTHOR]