Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017, Article ID 8576829, 13 pages
Research Article

PHUIMUS: A Potential High Utility Itemsets Mining Algorithm Based on Stream Data with Uncertainty

Air Force Engineering University, Xi’an, China

Correspondence should be addressed to Ju Wang; moc.kooltuo@ekujgnilgnoy

Received 8 October 2016; Revised 16 February 2017; Accepted 23 February 2017; Published 16 March 2017

Academic Editor: Haipeng Peng

Copyright © 2017 Ju Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


High utility itemsets (HUIs) mining has been a hot topic recently, which can be used to mine the profitable itemsets by considering both the quantity and profit factors. Up to now, researches on HUIs mining over uncertain datasets and data stream had been studied respectively. However, to the best of our knowledge, the issue of HUIs mining over uncertain data stream is seldom studied. In this paper, PHUIMUS (potential high utility itemsets mining over uncertain data stream) algorithm is proposed to mine potential high utility itemsets (PHUIs) that represent the itemsets with high utilities and high existential probabilities over uncertain data stream based on sliding windows. To realize the algorithm, potential utility list over uncertain data stream (PUS-list) is designed to mine PHUIs without rescanning the analyzed uncertain data stream. And transaction weighted probability and utility tree (TWPUS-tree) over uncertain data stream is also designed to decrease the number of candidate itemsets generated by the PHUIMUS algorithm. Substantial experiments are conducted in terms of run-time, number of discovered PHUIs, memory consumption, and scalability on real-life and synthetic databases. The results show that our proposed algorithm is reasonable and acceptable for mining meaningful PHUIs from uncertain data streams.