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Abstract: 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 (Maximal-Frequent Itemsets Mining), for mining recent maximal frequent itemsets from a high-speed stream of transactions within a sliding window. According to our algorithm, whenever a new transaction is inserted in the current window only its maximum itemset should be inserted into a prefix tree-based summary data structure called Max-Set for maintaining the number of independent appearance of each transaction in the current window. Finally, the set of recent maximal frequent itemsets is obtained from the current Max-Set. Experimental studies show that the proposed Max-FISM algorithm is highly efficient in terms of memory and time complexity for mining recent maximal frequent itemsets over high-speed data streams. [Copyright &y& Elsevier]

Feature selection techniques have been presented to allow us to choose a small subset of the original components' relevant features by removing irrelevant or redundant features. Feature selection is essential for many reasons such as simplification, performance, computational efficiency, and quality interpretability. Owing to the importance mentioned above, many researchers have proposed and developed many algorithms to solve the feature selection problem. Although these approaches produce useful results, they possess some shortcomings like inadequate feature reduction. In this paper, a novel feature selection algorithm based on the crow search algorithm is presented. The algorithm uses dynamic awareness probability to keep the balance between the local and global search processes. Moreover, a novel neighborhood assigning strategy has been introduced to optimize the local search. Considering the best-selected features in each iteration helps attain more benefits in global search. The main superiority of the proposed algorithm is the significant feature reduction along with retaining the accuracy. Compared to enhanced crow search algorithm, the proposed algorithm has improved the feature reduction metric and fitness metric by 27.12% and 5.16%, respectively, while losing the accuracy metric by only 0.53%. Several popular UCI datasets have been employed to evaluate the proposed feature selection algorithm. The experimental results show that the proposed algorithm outperformed other feature selection algorithms in state-of-the-art related works regarding feature reduction and accuracy. • Improve the balance between the local and global search. • Introducing a new neighborhood concept for improving the local search. • Proposing a new method to search more purposeful during exploration. • Increasing convergence rate using chaos. • Being pioneer in reducing the dataset volume. [ABSTRACT FROM AUTHOR]