Seeing the forest for the trees: tree-based uncertain frequent pattern mining

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dc.contributor.authorMacKinnon, Richard Kyle
dc.contributor.examiningcommitteeWang, Yang (Computer Science) Wang, Xikui (Statistics)en_US
dc.contributor.supervisorLeung, Carson K.-S. (Computer Science)en_US
dc.date.accessioned2016-01-13T22:43:27Z
dc.date.available2016-01-13T22:43:27Z
dc.date.issued2014-05en_US
dc.date.issued2014-09en_US
dc.date.issued2014-09en_US
dc.date.issued2014-12en_US
dc.date.issued2014-12en_US
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractMany frequent pattern mining algorithms operate on precise data, where each data point is an exact accounting of a phenomena (e.g., I have exactly two sisters). Alas, reasoning this way is a simplification for many real world observations. Measurements, predictions, environmental factors, human error, &ct. all introduce a degree of uncertainty into the mix. Tree-based frequent pattern mining algorithms such as FP-growth are particularly efficient due to their compact in-memory representations of the input database, but their uncertain extensions can require many more tree nodes. I propose new algorithms with tightened upper bounds to expected support, Tube-S and Tube-P, which mine frequent patterns from uncertain data. Extensive experimentation and analysis on datasets with different probability distributions are undertaken that show the tightness of my bounds in different situations.en_US
dc.description.noteFebruary 2016en_US
dc.identifier.citationMacKinnon, R.K., Leung, C.K.-S., Tanbeer, S.K. (2014) A scalable data analytics algorithm for mining frequent patterns from uncertain data. In Proc. PAKDDW 2014: 404-416. Springer International Publishing.en_US
dc.identifier.citationLeung, C.K.-S., MacKinnon, R.K. (2014) BLIMP: a compact tree structure for uncertain frequent pattern mining. In Proc. DaWaK 2014: 115-123. Springer International Publishing.en_US
dc.identifier.citationLeung, C.K.-S., MacKinnon, R.K., Tanbeer, S.K. (2014) Tightening upper bounds to the expected support for uncertain frequent pattern mining. In Proc. KES 2014: 328-337. Elsevier.en_US
dc.identifier.citationMacKinnon, R.K., Strauss, T.D., Leung, C.K.-S. (2014) DISC: efficient uncertain frequent pattern mining with tightened upper bounds. In Proc. ICDMW 2014: 1038-1045. IEEE Computer Society Press.en_US
dc.identifier.citationLeung, C.K.-S., MacKinnon, R.K., Tanbeer, S.K. (2014) Fast algorithms for frequent itemset mining from uncertain data. In Proc. ICDM 2014: 893-898. IEEE Computer Society Press.en_US
dc.identifier.urihttp://hdl.handle.net/1993/31059
dc.language.isoengen_US
dc.publisherSpringer International Publishingen_US
dc.publisherSpringer International Publishingen_US
dc.publisherElsevieren_US
dc.publisherIEEE Computer Society Pressen_US
dc.publisherIEEE Computer Society Pressen_US
dc.rightsopen accessen_US
dc.subjectData miningen_US
dc.subjectDatabasesen_US
dc.titleSeeing the forest for the trees: tree-based uncertain frequent pattern miningen_US
dc.typemaster thesisen_US
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