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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52311

Title: An efficient method for mining high utility closed itemsets
Authors: Loan T.T.Nguyen
Vinh V.Vu
Mi T.H.Lam
Thuy T.M.Duong
Ly T.Manh
Thuy T.T.Nguyen
Bay Vo
Hamido Fujita
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Data mining
High-utility pattern
High-utility closed pattern
Early pruning strategies
Backward checking
Forward checking
Date: 2019-08
Issue Date: 2019-11-18T01:30:29Z
Publisher: Information Sciences
Abstract: Abstract: Mining closed high utility itemsets (CHUIs) involves finding a representative set of HUIs that is usually smaller than that of HUIs but can generate the full HUIs without loss of information. Researchers have therefore shown interest in this problem, and many methods have been proposed for mining CHUIs effectively, of which CHUI-Miner and EFIM-Closed are the two most efficient algorithms. However, these face performance issues when mining CHUIs from sparse datasets. In this paper, we propose a method for the effective mining of CHUIs in both dense and sparse datasets. We first modify the compact utility list structure in the HMiner algorithm to reduce the mining time, and then develop backward and forward checking methods using the most recently explored CHUIs and combine this with candidate building for the next levels. Finally, we apply pruning strategies to reduce the search space for the generation of CHUIs. Our experimental results show that the proposed algorithm, called HMiner-Closed, is more efficient than the state-of-the-art algorithms for both dense and sparse datasets.
Relation: 495 pp.78-99
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52311
Appears in Collections:[資訊工程學系] 期刊論文

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