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2014 IEEE International Conference on Granular Computing (GrC) (2014)
Noboribetsu, Japan
Oct. 22, 2014 to Oct. 24, 2014
ISBN: 978-1-4799-5464-3
pp: 155-160
Chia Hua Li , Departement of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC
Cheng-Wei Wu , Departement of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC
Vincent S. Tseng , Departement of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC
ABSTRACT
High utility quantitative itemset mining refers to discovering sets of items that carry not only high utilities (e.g., high profits) but also quantitative attributes. Although this topic is very important to many applications, it has not been deeply explored and existing algorithms for mining high utility quantitative itemsets remain computationally expensive. To address this problem, we propose a novel algorithm named VHUQI (Vertical mining of High Utility Quantitative Itemsets) for efficiently mining high utility quantitative itemsets in databases. VHUQI adopts a vertical representation to maintain the utility information of itemsets in databases with several effective strategies integrated to prune the search space. The experimental results on both real and synthetic datasets show that VHUQI outperforms the state-of-the-art algorithms substantially in terms of both execution time and memory consumption.
INDEX TERMS
Itemsets, Memory management, Algorithm design and analysis, Explosions, Space exploration, Conferences,utility mining, Quantitative itemset mining, high utility itemset mining, high utility quantitative itemset mining
CITATION
Chia Hua Li, Cheng-Wei Wu, Vincent S. Tseng, "Efficient vertical mining of high utility quantitative itemsets", 2014 IEEE International Conference on Granular Computing (GrC), vol. 00, no. , pp. 155-160, 2014, doi:10.1109/GRC.2014.6982826
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