SOHUPDS: A single-pass one-phase algorithm for mining high utility patterns over a data stream

Bijay Prasad Jaysawal, Jen Wei Huang

研究成果: Conference contribution

11 引文 斯高帕斯(Scopus)

摘要

High utility pattern mining has emerged to overcome the limitation of frequent pattern mining where only frequency is taken as importance without considering the actual importance of items. Existing algorithms for mining high utility patterns over a data stream are two-phase algorithms that are not scalable due to the large number of candidates generation in the first phase, particularly when the minimum utility threshold is low. Moreover, in the second phase, the algorithm needs to scan the database again to find out actual utility for candidates. In this paper, we propose a novel algorithm SOHUPDS to mine high utility patterns over a data stream with the sliding window technique using the projected database approach. In addition, we propose a data structure IUDataListSW, which stores utility and upper-bound values of the items in the current sliding window. Moreover, IUDataListSW stores position of items in the transaction to get the initial projected database of items efficiently. Furthermore, we propose an update strategy to utilize mined high utility patterns from the previous sliding window to update high utility patterns in the current sliding window. Therefore, SOHUPDS is able to mine high utility patterns over a data stream in a single pass and one phase. Experimental results illustrate that SOHUPDS is more efficient than the state-of-the-art algorithms in terms of execution time as well as memory usage.

原文English
主出版物標題35th Annual ACM Symposium on Applied Computing, SAC 2020
發行者Association for Computing Machinery
頁面490-497
頁數8
ISBN(電子)9781450368667
DOIs
出版狀態Published - 2020 3月 30
事件35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic
持續時間: 2020 3月 302020 4月 3

出版系列

名字Proceedings of the ACM Symposium on Applied Computing

Conference

Conference35th Annual ACM Symposium on Applied Computing, SAC 2020
國家/地區Czech Republic
城市Brno
期間20-03-3020-04-03

All Science Journal Classification (ASJC) codes

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