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Max-FISM: Mining (recently) maximal frequent itemsets over data streams using the sliding window model
2012
Computers and Mathematics with Applications
Frequent itemset mining from data streams is an important data mining problem with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. However, it is also a difficult problem due to the unbounded, high-speed and continuous characteristics of streaming data. Therefore, extracting frequent itemsets from more recent data can enhance the analysis of stream data. In this paper, we propose an efficient algorithm, called Max-FISM
doi:10.1016/j.camwa.2012.01.045
fatcat:3lduoonbwjb6jda3yj62cys2o4