Tightening upper bounds to the expected support for uncertain frequent pattern mining

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dc.contributor.authorLeung, Carson K.
dc.contributor.authorMacKinnon, Richard Kyle
dc.contributor.authorTanbeer, Syed K.
dc.date.accessioned2017-02-13T15:17:21Z
dc.date.available2017-02-13T15:17:21Z
dc.date.issued2014
dc.descriptionC.K. Leung, R.K. MacKinnon, S.K. Tanbeer. Tightening upper bounds to the expected support for uncertain frequent pattern mining. Procedia Computer Science, 35 (2014), pp. 328-337. 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.abstractDue to advances in technology, high volumes of valuable data can be collected and transmitted at high velocity in various scientific and engineering applications. Consequently, efficient data mining algorithms are in demand for analyzing these data. For instance, frequent pattern mining discovers implicit, previously unknown, and potentially useful knowledge about relationships among frequently co-occurring items, objects and/or events. While many frequent pattern mining algorithms handle precise data, there are situations in which data are uncertain. In recent years, tree-based algorithms for mining uncertain data have been developed. However, tree structures corresponding to these algorithms can be large. Other tree structures for handling uncertain data may achieve compactness at the expense of loose upper bounds on expected supports. In this paper, we propose (i) a compact tree structure for capturing uncertain data, (ii) a technique for using our tree structure to tighten upper bounds to expected support, and (iii) an algorithm for mining frequent patterns based on our tightened bounds. Experimental results show the benefits of our tightened upper bounds to expected supports in uncertain frequent pattern mining.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitobaen_US
dc.identifier.citationC.K. Leung, R.K. MacKinnon, S.K. Tanbeer. Tightening upper bounds to the expected support for uncertain frequent pattern mining. Procedia Computer Science, 35 (2014), pp. 328-337.en_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.procs.2014.08.113
dc.identifier.urihttp://hdl.handle.net/1993/32122
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsopen accessen_US
dc.subjectdata miningen_US
dc.subjectdata structureen_US
dc.subjectexpected supporten_US
dc.subjectfrequent pattern miningen_US
dc.subjectknowledge discoveryen_US
dc.subjecttree-based miningen_US
dc.subjectuncertain dataen_US
dc.titleTightening upper bounds to the expected support for uncertain frequent pattern miningen_US
dc.typeArticleen_US
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