Dynamically mining frequent patterns over online data streams

Authors:
Xuejun Liu;Hongbing Xu;Yisheng Dong;Yongli Wang;Jiangbo Qian
Affiliations:
Department of Computer Science and Technology, Southeast University, Nanjing, China;Department of Computer Science and Technology, Southeast University, Nanjing, China;Department of Computer Science and Technology, Southeast University, Nanjing, China;Department of Computer Science and Technology, Southeast University, Nanjing, China;Department of Computer Science and Technology, Southeast University, Nanjing, China
Venue:
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
Year:
2005

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Abstract

Data streams are massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, it is challenge to find frequent items over data streams in a dynamic environment. In this paper, a new novel algorithm was proposed, which can capture frequent items with any length online continuously. Furthermore, several optimization techniques are devised to minimize processing time as well as main memory usage. Compared with related algorithm, it is more suitable for the mining of long frequent items. Finally, the proposed method is analyzed by a series of experiments and the results show that this algorithm owns significantly better performance than before.