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dc.contributor.authorCuzzocrea, Alfredo
dc.contributor.authorLeung, Carson K.
dc.contributor.authorMacKinnon, Richard Kyle
dc.date.accessioned2016-03-08T21:50:38Z
dc.date.available2016-03-08T21:50:38Z
dc.date.issued2015
dc.identifier.citationA. Cuzzocrea, C.K. Leung, R.K. MacKinnon. Approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data. Procedia Computer Science, 60 (2015), pp. 613-622en_US
dc.identifier.urihttp://hdl.handle.net/1993/31150
dc.descriptionA. Cuzzocrea, C.K. Leung, R.K. MacKinnon. Approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data. Procedia Computer Science, 60 (2015), pp. 613-622. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.description.abstractKnowledge discovery and data mining generally discovers implicit, previously unknown, and useful knowledge from data. As one of the popular knowledge discovery and data mining tasks, frequent itemset mining, in particular, discovers knowledge in the form of sets of frequently co-occurring items, events, or objects. On the one hand, in many real-life applications, users mine frequent patterns from traditional databases of precise data, in which users know certainly the presence of items in transactions. On the other hand, in many other real-life applications, users mine frequent itemsets from probabilistic sets of uncertain data, in which users are uncertain about the likelihood of the presence of items in transactions. Each item in these probabilistic sets of uncertain data is often associated with an existential probability expressing the likelihood of its presence in that transaction. To mine frequent itemsets from these probabilistic datasets, many existing algorithms capture lots of information to compute expected support. To reduce the amount of space required, algorithms capture some but not all information in computing or approximating expected support. The tradeoff is that the upper bounds to expected support may not be tight. In this paper, we examine several upper bounds and recommend to the user which ones consume less space while providing good approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitobaen_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectknowledge discovery and data miningen_US
dc.subjectexpected supporten_US
dc.subjectfrequent patternsen_US
dc.subjectuncertain dataen_US
dc.subjectupper boundsen_US
dc.titleApproximation to expected support of frequent itemsets in mining probabilistic sets of uncertain dataen_US
dc.typeArticleen_US
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doihttp://dx.doi.org/10.1016/j.procs.2015.08.195


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