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Annals of Data Science
Published in: Annals of Data Science 3/2016

01-09-2016

Discovering Productive Periodic Frequent Patterns in Transactional Databases

Author: Vincent Mwintieru Nofong

Published in: Annals of Data Science | Issue 3/2016

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Abstract

Periodic frequent pattern mining is an important data mining task for various decision making. However, it often presents a large number of periodic frequent patterns, most of which are not useful as their periodicities are due to random occurrence of uncorrelated items. Such periodic frequent patterns would most often be detrimental in decision making where correlations between the items of periodic frequent patterns are vital. To enable mine the periodic frequent patterns with correlated items, we employ a correlation test on periodic frequent patterns and introduce the productive periodic frequent patterns as the set of periodic frequent patterns with correlated items. We finally develop the productive periodic frequent pattern (PPFP) framework for mining our introduced productive periodic frequent patterns. PPFP is efficient and the productiveness measure removes the periodic frequent patterns with uncorrelated items.
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Metadata
Title
Discovering Productive Periodic Frequent Patterns in Transactional Databases
Author
Vincent Mwintieru Nofong
Publication date
01-09-2016
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 3/2016
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-016-0078-8

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