Abstract
Tree structures (e.g., UF-trees, UFP-trees) corresponding to many existing uncertain frequent pattern mining algorithms can be large. Other tree structures for handling uncertain data may achieve compactness at the expense of loose upper bounds on expected supports. To solve this problem, we propose a compact tree structure that captures uncertain data with tighter upper bounds than the aforementioned tree structures. The corresponding algorithm mines frequent patterns from this compact tree structure. Experimental results show the compactness of our tree structure and the tightness of upper bounds to expected supports provided by our uncertain frequent pattern mining algorithm.
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Leung, C.KS., MacKinnon, R.K. (2014). BLIMP: A Compact Tree Structure for Uncertain Frequent Pattern Mining. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham. https://doi.org/10.1007/978-3-319-10160-6_11
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DOI: https://doi.org/10.1007/978-3-319-10160-6_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10159-0
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