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Discovering Productive Periodic Frequent Patterns in Transactional Databases

Discovering Productive Periodic Frequent Patterns in Transactional Databases 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Discovering Productive Periodic Frequent Patterns in Transactional Databases

Nofong, Vincent
Annals of Data Science , Volume 3 (3) – Apr 23, 2016
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15 pages

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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Business and Management; Business and Management, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Computing Methodologies
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-016-0078-8
Publisher site
See Article on Publisher Site

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.

Journal

Annals of Data Science – Springer Journals

Published: Apr 23, 2016

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