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dc.contributor.author Leung, Carson K.
dc.contributor.author MacKinnon, Richard Kyle
dc.contributor.author Tanbeer, Syed K.
dc.date.accessioned 2017-02-13T15:17:21Z
dc.date.available 2017-02-13T15:17:21Z
dc.date.issued 2014
dc.identifier.citation C.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.uri http://hdl.handle.net/1993/32122
dc.description C.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.abstract Due 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.sponsorship Natural Sciences and Engineering Research Council of Canada (NSERC); University of Manitoba en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject data mining en_US
dc.subject data structure en_US
dc.subject expected support en_US
dc.subject frequent pattern mining en_US
dc.subject knowledge discovery en_US
dc.subject tree-based mining en_US
dc.subject uncertain data en_US
dc.title Tightening upper bounds to the expected support for uncertain frequent pattern mining en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.1016/j.procs.2014.08.113


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