Title: Pursuing efficient data stream mining by removing long patterns from summaries

Authors: Po-Jen Chuang; Yun-Sheng Tu

Addresses: Department of Electrical and Computer Engineering, Tamkang University, Tamsui, New Taipei City, 25137, Taiwan ' Department of Electrical and Computer Engineering, Tamkang University, Tamsui, New Taipei City, 25137, Taiwan

Abstract: Frequent pattern mining is a useful data mining technique. It can help in digging out frequently used patterns from the massive internet data streams for significant applications and analyses. To uplift the mining accuracy and reduce the needed processing time, this paper proposes a new approach that is able to remove less used long patterns from the pattern summary to preserve space for more frequently used short patterns, in order to enhance the performance of existing frequent pattern mining algorithms. Extensive simulation runs are carried out to check the performance of the proposed approach. The results show that our approach can strengthen the mining performance by effectively bringing down the required run time and substantially increasing the mining accuracy.

Keywords: data streams; frequent pattern mining; pattern summary; length skip; performance evaluation.

DOI: 10.1504/IJDMMM.2021.119630

International Journal of Data Mining, Modelling and Management, 2021 Vol.13 No.4, pp.388 - 409

Received: 12 Oct 2019
Accepted: 13 Jun 2020
Published online: 13 Dec 2021 *

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