Abstract The huge number of data streams makes it impossible to mine recent frequent itemsets. Due to the maximal frequent itemsets can perfectly imply all the frequent itemsets and the number is much smaller, therefore, the time cost and the memory usage for mining maximal frequent itemsets are much more efficient. This paper proposes an improved method called Recent Maximal Frequent Itemsets Mining (RMFIsM) to mine recent maximal frequent itemsets over data streams with sliding window. The RMFIsM method uses two matrixes to store the information of data streams, the first matrix stores the information of each transaction and the second one stores the frequent 1-itemsets. The frequent p-itemsets are mined with “extension” process of frequent 2-itemsets, and the maximal frequent itemsets are obtained by deleting the sub-itemsets of long frequent itemsets. Finally, the performance of the RMFIsM method is conducted by a series of experiments, the results show that the proposed RMFIsM method can mine recent maximal frequent itemsets efficiently.

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[13] Yang J., Wei Y., and Zhou F., “An Efficient Algorithm for Mining Maximal Frequent Patterns over Data Streams,” in Proceedings of 7th International Conference on Intelligent Human- Machine Systems and Cybernetics, Hangzhou, pp. 444-447, 2015. Saihua Cai is a Ph.D. student in College of Information and Electrical Engineering, China Agricultural University, China. He received the MS degree from Jiangsu University, China, in 2016. His major research interests include uncertain data management, data mining, outlier detecting and software testing. Shangbo Hao is a Master Student in College of Information and Electrical Engineering, China Agricultural University, China. His research interests include pattern mining and outlier detecting. Ruizhi Sun is a Full Professor in College of Information and Electrical Engineering, China Agricultural University, China. He received his Ph.D. degree in Computer Science and Technology from Tsinghua University, Beijing, China, in 2003. His major research interests include agricultural data acquisition and processing technology, computer network and applications, workflow management and cloud computing. Gang Wu is an associate professor in Secretary of Computer Science Department, Tarim University, China. His research interests mainly involve agriculture information processing technology, data mining, agricultural remote sensing application.