Pursuing Efficient Data Stream Mining by Removing Long Patterns from Summaries
學年 110
學期 1
出版（發表）日期 2021-12-07
作品名稱 Pursuing Efficient Data Stream Mining by Removing Long Patterns from Summaries
作品名稱（其他語言）
著者 Po-Jen Chuang; Yun-Sheng Tu
單位
出版者
著錄名稱、卷期、頁數 International Journal of Data Mining, Modelling and Management 13(4), p.388-409
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.
關鍵字 data streams;frequent pattern mining;pattern summary;length skip;performance evaluation
語言 en
ISSN 1759-1163; 1759-1171
期刊性質 國外
收錄於 ESCI
產學合作
通訊作者
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版,紙本
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