Pursuing Efficient Data Stream Mining by Removing Long Patterns from Summaries

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/121649

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题名:	Pursuing Efficient Data Stream Mining by Removing Long Patterns from Summaries
作者:	Po-Jen Chuang;Yun-Sheng Tu
关键词:	data streams;frequent pattern mining;pattern summary;length skip;performance evaluation
日期:	2021-12-07
上传时间:	2021-12-15 17:02:26 (UTC+8)
出版者:	Inderscience Publishers
摘要:	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.
關聯:	International Journal of Data Mining, Modelling and Management 13(4), p.388-409
DOI:	10.1504/IJDMMM.2021.119630
显示于类别:	[電機工程學系暨研究所] 期刊論文

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