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

Many 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. / February 2016

Identiferoai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/31059
Date12 1900
CreatorsMacKinnon, Richard Kyle
ContributorsLeung, Carson K.-S. (Computer Science), Wang, Yang (Computer Science) Wang, Xikui (Statistics)
PublisherSpringer International Publishing, Springer International Publishing, Elsevier, IEEE Computer Society Press, IEEE Computer Society Press
Source SetsUniversity of Manitoba Canada
Detected LanguageEnglish

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