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.
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Liu, X., Xu, H., Dong, Y., Wang, Y., Qian, J. (2005). Dynamically Mining Frequent Patterns over Online Data Streams. In: Pan, Y., Chen, D., Guo, M., Cao, J., Dongarra, J. (eds) Parallel and Distributed Processing and Applications. ISPA 2005. Lecture Notes in Computer Science, vol 3758. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11576235_65
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DOI: https://doi.org/10.1007/11576235_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29769-7
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