标题:Updating high average-utility itemsets with pre-large concept
作者:Wu, Jimmy Ming-Tai; Teng, Qian; Lin, Jerry Chun-Wei; Yun, Unil; Chen, Hsing-Chung
通讯作者:Lin, Jerry ChunWei
作者机构:[Wu, Jimmy Ming-Tai; Teng, Qian] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Peoples R China.; [Lin, Jerry Chun-Wei] Western Norway 更多
来源:JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
出版年:2020
卷:38
期:5
页码:5831-5840
DOI:10.3233/JIFS-179670
关键词:pre-large; high average-utility itemset mining; dynamic database;; incremental; transaction insertion
摘要:HAUIM (High Average-Utility Itemset Mining) is a variation of HUIM (High-Utility Itemset Mining) that provides a reliable measure to reveal utility patterns in light of the length of the mined pattern. Several works have been studied to improve mining efficiency by designing multiple pruning strategies and efficient frameworks, but fewer studies have centered on the sophisticated database maintenance algorithm. Existing works still have to rescan the databases multiple times when it is necessary. We first use the pre-large principle in this paper to efficiently update the newly discovered HAUIs. For further updates and maintenance on the basis of the two thresholds, the Pre-large Average Utility Itemset (PAUI) can be maintained to increase the mining performance. Experiments will then be performed to compare the batch model, the Fast-Updated (FUP)-based model, and the Apriori-like HAUIM (APHAUIM) model designed in respect of the number of maintenance patterns, scalability, runtime, and memory usage.
收录类别:EI;SCOPUS;SCIE
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086713357&doi=10.3233%2fJIFS-179670&partnerID=40&md5=827f7fa4a8cd15ba39d263569750770d
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