Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yao, Q., Huang, X., An, A. (2005). A Machine Learning Approach to Identifying Database Sessions Using Unlabeled Data. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_25
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DOI: https://doi.org/10.1007/11546849_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
Online ISBN: 978-3-540-31732-6
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