Efficient vertical mining of high utility quantitative itemsets

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 u...

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Bibliographic Details
Published in2014 IEEE International Conference on Granular Computing (GrC) pp. 155 - 160
Main Authors Chia Hua Li, Cheng-Wei Wu, Tseng, Vincent S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2014
Subjects
Algorithm design and analysis
Conferences
Explosions
high utility itemset mining
high utility quantitative itemset mining
Itemsets
Memory management
Quantitative itemset mining
Space exploration
utility mining
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Summary: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.
DOI:10.1109/GRC.2014.6982826