文章基本信息

标题：An Algorithm of Top-k High Utility Itemsets Mining over Data Stream
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作者：Lu, Tianjun ; Liu, Yang ; Wang, Le 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2014
卷号：9
期号：9
页码：2342-2347
DOI：10.4304/jsw.9.9.2342-2347
语种：English
出版社：Academy Publisher
摘要：Existing top-k high utility itemset (HUI) mining algorithms generate candidate itemsets in the mining process; their time & space performance might be severely affected when the dataset is large or contains many long transactions; and when applied to data streams, the performance of corresponding mining algorithm is especially crucial. To address this issue, propose a sliding window based top-k HUIs mining algorithm TOPK-SW; it first stores each batch data of current window as well as the items’ utility information to a tree called HUI-Tree, which ensures effective retrieval of utility values without re-scan the dataset, so as to efficiently improve the mining performance. TOPK-SW was tested on 4 classical datasets; results show that TOPK-SW outperforms existing algorithms significantly in both time and space efficiency, especially the time performance improves over 1 order of magnitude.
关键词：data stream;high utility itemset;frequent itemset;data mining;top-k