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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Fuzzy Maximal Frequent Itemset Mining Over Quantitative Databases
verfasst von : Haifeng Li, Yue Wang, Ning Zhang, Yuejin Zhang
Erschienen in: Intelligent Information and Database Systems
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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.