Objective and Subjective Algorithms for Grouping Association Rules
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Dynamic web log session identification with statistical language models
Journal of the American Society for Information Science and Technology - Special issue: Webometrics
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
Incremental click-stream tree model: Learning from new users for web page prediction
Distributed and Parallel Databases
An efficient approach to mining indirect associations
Journal of Intelligent Information Systems
COWES: Web user clustering based on evolutionary web sessions
Data & Knowledge Engineering
Session boundary detection for association rule learning using n-gram language models
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Learning theory analysis for association rules and sequential event prediction
The Journal of Machine Learning Research
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We present our experience in mining web usage patternsfrom a large collection of Livelink log data. Livelink is aweb-based product of Open Text, which provides automaticmanagement and retrieval of different types of informationobjects over an intranet or extranet. We report our experiencein preprocessing raw log data and post-processing themining results for finding interesting rules. In particular,we compare and evaluate a number of rule interestingnessmeasures and find that two of the measures that have notbeen used in association rule learning work very well.