Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/20838
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dc.contributor.authorKrishnamoorthy, Srikumar
dc.date.accessioned2018-06-27T13:35:35Z
dc.date.available2018-06-27T13:35:35Z
dc.date.issued2017-12-30
dc.identifier.urihttp://hdl.handle.net/11718/20838
dc.descriptionExpert Systems With Applications 90 (2017) 168–183en_US
dc.description.abstractHigh utility itemset mining problem uses the notion of utilities to discover interesting and actionable patterns. Several data structures and heuristic methods have been proposed in the literature to efficiently mine high utility itemsets. This paper advances the state-of-the-art and presents HMiner, a high utility itemset mining method. HMiner utilizes a few novel ideas and presents a compact utility list and virtual hyperlink data structure for storing itemset information. It also makes use of several pruning strategies for efficiently mining high utility itemsets. The proposed ideas were evaluated on a set of benchmark sparse and dense datasets. The execution time improvements ranged from a modest thirty percent to three orders of magnitude across several benchmark datasets. The memory consumption requirements also showed up to an order of magnitude improvement over the state-of-the-art methods. In general, HMiner was found to work well in the dense regions of both sparse and dense benchmark datasets.en_US
dc.publisherElsevieren_US
dc.subjectHigh utility miningen_US
dc.subjectFrequent itemset miningen_US
dc.subjectData miningen_US
dc.titleHMiner: efficiently mining high utility itemsetsen_US
dc.typeArticleen_US
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