Abstract
Fuzzy frequent itemset mining is an important problem in quantitative data mining. In this paper, we define the problem of fuzzy maximal frequent itemset mining, which, to the best of our knowledge, has never been addressed before. A simple tree-based data structure called FuzzyTree is constructed, in which the fuzzy itemsets are sorted dynamically based the supports. Then, we propose an algorithm named FMFIMiner to build the FuzzyTree. In FMFIMiner, we can ignore processing the other children nodes once the supports between the parent node and one child node are equal; moreover, we conduct pruning the certain support computing by checking whether an itemset is in the final results. Theoretical analysis and experimental studies over 4 datasets demonstrate that our proposed algorithm can efficiently decrease the runtime and memory cost, and significantly outperform the baseline algorithm MaxFFI-Miner.
This research is supported by the National Natural Science Foundation of China (61100112,61309030), Beijing Higher Education Young Elite Teacher Project (YETP0987), Granted by Discipline Construction Foundation of Central University of Finance and Economics 2016XX05).
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Li, H., Wang, Y., Zhang, N., Zhang, Y. (2017). Fuzzy Maximal Frequent Itemset Mining Over Quantitative Databases. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_45
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DOI: https://doi.org/10.1007/978-3-319-54472-4_45
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