Abstract
Due to streaming data are infinite in length and fast changing with time, it is very significant to limit the memory usage in the process of mining data streams. Maximal frequent itemset is a subset of frequent itemsets; it can represent the important information of frequent itemsets with low computational cost. In this paper, we propose an algorithm MMFI_DSSW (Mining Maximal Frequent Itemsets in Data Streams Sliding Window) to mine maximal frequent itemsets with a novel MFI_BVT (Maximal Frequent Itemsets Binary Vector Table) summary data structure in sliding window. MFI_BVT builds a binary vector for each itemsets first. Then algorithm MMFI_DSSW performs logical AND operation to mine all the maximal frequent itemsets in MFI_BVT with a single-pass scan incoming data. Finally, the mining result can be updated incrementally. Experiment shows that algorithm MMFI_DSSW is efficient and scalable in memory usage and running time of CPU.
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Feng, J., Ren, J. (2007). MMFI_DSSW – A New Method to Incrementally Mine Maximal Frequent Itemsets in Transaction Sensitive Sliding Window. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_47
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DOI: https://doi.org/10.1007/978-3-540-76719-0_47
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