Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Email classification with co-training
CASCON '01 Proceedings of the 2001 conference of the Centre for Advanced Studies on Collaborative research
Dynamic web log session identification with statistical language models
Journal of the American Society for Information Science and Technology - Special issue: Webometrics
Finding and analyzing database user sessions
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Data mining-based materialized view and index selection in data warehouses
Journal of Intelligent Information Systems
Segmenting and labeling query sequences in a multidatabase environment
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Mining and modeling database user access patterns
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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In this paper, we describe a novel co-training based algorithm for identifying database user sessions from database traces. The algorithm learns to identify positive data (session boundaries) and negative data (non-session boundaries) incrementally by using two methods interactively in several iterations. In each iteration, previous identified positive and negative data are used to build better models, which in turn can label some new data and improve performance of further iterations. We also present experimental results.