TY - GEN
T1 - An efficient algorithm for mining high utility quantitative itemsets
AU - Li, Chia Hua
AU - Wu, Cheng Wei
AU - Huang, Jian Tao
AU - Tseng, Vincent S.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Mining high utility quantitative itemsets (HUQIs) is now a novel research topic in data mining field, which consists of discovering sets of items having a high utility (e.g. high profit) and providing information about quantities of items in each itemset. In market analysis, it could supply for decision-makers that shopping behavior could bring high profit to the company. For example, the customers purchase M to N units of a product A and purchase P to Q units of a product B at the same time. However, mining HUQIs using existing algorithms remains very computationally expensive and makes the results hard to be utilized by users. In view of this, we propose a novel algorithm named HUQI-Miner (High Utility Quantitative Itemsets Miner) for efficiently mining HUQIs in databases. Experimental results on both real and synthetic datasets show that HUQI-Miner outperforms the state-of-the-art algorithms in terms of both execution time and memory usage.
AB - Mining high utility quantitative itemsets (HUQIs) is now a novel research topic in data mining field, which consists of discovering sets of items having a high utility (e.g. high profit) and providing information about quantities of items in each itemset. In market analysis, it could supply for decision-makers that shopping behavior could bring high profit to the company. For example, the customers purchase M to N units of a product A and purchase P to Q units of a product B at the same time. However, mining HUQIs using existing algorithms remains very computationally expensive and makes the results hard to be utilized by users. In view of this, we propose a novel algorithm named HUQI-Miner (High Utility Quantitative Itemsets Miner) for efficiently mining HUQIs in databases. Experimental results on both real and synthetic datasets show that HUQI-Miner outperforms the state-of-the-art algorithms in terms of both execution time and memory usage.
KW - High utility itemset mining
KW - High utility quantitative itemset mining
KW - Quantitative itemset mining
KW - Utility mining
UR - http://www.scopus.com/inward/record.url?scp=85078735607&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2019.00145
DO - 10.1109/ICDMW.2019.00145
M3 - Conference contribution
AN - SCOPUS:85078735607
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 1005
EP - 1012
BT - Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
A2 - Papapetrou, Panagiotis
A2 - Cheng, Xueqi
A2 - He, Qing
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Y2 - 8 November 2019 through 11 November 2019
ER -