Pursuing efficient data stream mining by removing long patterns from summaries

Po-Jen Chuang and Yun-Sheng Tu

International Journal of Data Mining, Modelling and Management, 2021, vol. 13, issue 4, 388-409

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. (search for similar items in EconPapers)
Date: 2021
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