Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
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 usage mining based on probabilistic latent semantic analysis
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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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 $\mathcal{(WAS)}s$ 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 $\mathcal{WAS}s$ are event/task-driven. As a result, $\mathcal{WAS}s$ 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.