Knowledge discovery or extracting knowledge from large amount of data is a desirable task in competitive businesses. Data mining is an essential step in knowledge discovery process. Frequent patterns play an important role in data mining tasks such as clustering, classification, and prediction and association analysis. However, the mining of all frequent patterns will lead to a massive number of patterns. A reasonable solution is identifying maximal frequent patterns which form the smallest representative set of patterns to generate all frequent patterns. This research proposes a new method for mining maximal frequent patterns. The method includes an efficient database encoding technique, a novel tree structure called PC_Tree and PCMiner algorithm. Experiment results verify the compactness and performance.
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Nadimi-Shahraki, M., Mustapha, N., Sulaiman, M.N.B., Mamat, A.B. (2009). PC_Tree: Prime-Based and Compressed Tree for Maximal Frequent Patterns Mining. In: Ao, SI., Gelman, L. (eds) Advances in Electrical Engineering and Computational Science. Lecture Notes in Electrical Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2311-7_42
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DOI: https://doi.org/10.1007/978-90-481-2311-7_42
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