Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
An efficient algorithm for frequent itemset mining on data streams
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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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 SlidingWindow) 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.