• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

High Utility Itemset Mining over Uncertain Datasets Based on a Quantum Genetic Algorithm

Vol. 12, No. 8, August 30, 2018
10.3837/tiis.2018.08.005 , Download Paper (Free):

Abstract

The discovered high potential utility itemsets (HPUIs) have significant influence on a variety of areas, such as retail marketing, web click analysis, and biological gene analysis. Thus, in this paper, we propose an algorithm called HPUIM-QGA (Mining high potential utility itemsets based on a quantum genetic algorithm) to mine HPUIs over uncertain datasets based on a quantum genetic algorithm (QGA). The proposed algorithm not only can handle the problem of the non-downward closure property by developing an upper bound of the potential utility (UBPU) (which prunes the unpromising itemsets in the early stage) but can also handle the problem of combinatorial explosion by introducing a QGA, which finds optimal solutions quickly and needs to set only very few parameters. Furthermore, a pruning strategy has been designed to avoid the meaningless and redundant itemsets that are generated in the evolution process of the QGA. As proof of the HPUIM-QGA, a substantial number of experiments are performed on the runtime, memory usage, analysis of the discovered itemsets and the convergence on real-life and synthetic datasets. The results show that our proposed algorithm is reasonable and acceptable for mining meaningful HPUIs from uncertain datasets.


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Cite this article

[IEEE Style]
J. Wang, F. Liu and C. Jin, "High Utility Itemset Mining over Uncertain Datasets Based on a Quantum Genetic Algorithm," KSII Transactions on Internet and Information Systems, vol. 12, no. 8, pp. 3606-3629, 2018. DOI: 10.3837/tiis.2018.08.005 .

[ACM Style]
Ju Wang, Fuxian Liu, and Chunjie Jin. 2018. High Utility Itemset Mining over Uncertain Datasets Based on a Quantum Genetic Algorithm. KSII Transactions on Internet and Information Systems, 12, 8, (2018), 3606-3629. DOI: 10.3837/tiis.2018.08.005 .