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Topics in Association Rules

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dc.contributor.advisor McNicholas, Paul
dc.contributor.author Shaikh, Mateen
dc.date.accessioned 2013-06-21T17:25:29Z
dc.date.available 2013-06-21T17:25:29Z
dc.date.copyright 2013-06
dc.date.created 2013-06-05
dc.date.issued 2013-06-21
dc.identifier.uri http://hdl.handle.net/10214/7250
dc.description.abstract Association rules are a useful concept in data mining with the goal of summa- rizing the strong patterns that exist in data. We have identified several issues in mining association rules and addressed them in three main areas. The first area we explore is standardized interestingness measures. Different interestingness measures exist on different ranges, and interpreting them can be subtly problematic. We standardize several interestingness measures and show how these are useful to consider in association rule mining in three examples. A second area we address is incomplete transactions. By applying statistical methods in new ways to association rules, we provide a more comprehensive means of analyzing incomplete transactions. We also describe how to find families of distributions for interestingness measure values when transactions are incomplete. Finally, we address the common result of mining: a plethora of association rules. Unlike methods which attempt to reduce the number of resulting rules, we harness this large quantity to find a higher-level set of patterns. en_US
dc.description.sponsorship NSERC Discovery Grant and OMRI Early Researcher Award en_US
dc.language.iso en en_US
dc.rights Attribution-NonCommercial-NoDerivs 2.5 Canada *
dc.rights Attribution-NonCommercial-NoDerivs 2.5 Canada *
dc.rights Attribution-NonCommercial-NoDerivs 2.5 Canada *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/2.5/ca/ *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/2.5/ca/ *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/2.5/ca/ *
dc.subject Association Rules en_US
dc.subject Data Mining en_US
dc.subject Statistics en_US
dc.subject Missing Data en_US
dc.subject Hierarchies en_US
dc.subject Clustering en_US
dc.title Topics in Association Rules en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Doctor of Philosophy en_US
dc.degree.department Department of Mathematics and Statistics en_US
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