期刊論文
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 |
相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121649 ) |