Abstract
Many existing algorithms mine frequent patterns from traditional databases of precise data. However, there are situations in which data are uncertain. In recent years, researchers have paid attention to frequent pattern mining from uncertain data. When handling uncertain data, UF-growth and UFP-growth are examples of well-known mining algorithms, which use the UF-tree and the UFP-tree respectively. However, these trees can be large, and thus degrade the mining performance. In this paper, we propose (i) a more compact tree structure to capture uncertain data and (ii) an algorithm for mining all frequent patterns from the tree. Experimental results show that (i) our tree is usually more compact than the UF-tree or UFP-tree, (ii) our tree can be as compact as the FP-tree, and (iii) our mining algorithm finds frequent patterns efficiently.
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Leung, C.KS., Tanbeer, S.K. (2013). PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_2
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DOI: https://doi.org/10.1007/978-3-642-37453-1_2
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