Abstract
This paper addresses the maintenance of discovered frequent patterns when a batch of transactions are removed from the original dataset. We conduct an in-depth investigation on how the frequent pattern space evolves under transaction removal updates using the concept of equivalence classes. Inspired by the evolution analysis, an effective and exact algorithm TRUM is proposed to maintain frequent patterns. Experimental results demonstrate that our algorithm outperforms representative state-of-the-art algorithms.
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Feng, M., Dong, G., Li, J., Tan, YP., Wong, L. (2007). Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_50
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DOI: https://doi.org/10.1007/978-3-540-71701-0_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
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