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 PPFP, an efficient Productive Periodic Frequent Pattern mining framework. PPFP is efficient and the productiveness measure removes the periodic frequent patterns with uncorrelated items.
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Nofong, V.M. (2015). Discovering Productive Periodic Frequent Patterns in Transactional Databases. In: Zhang, C., et al. Data Science. ICDS 2015. Lecture Notes in Computer Science(), vol 9208. Springer, Cham. https://doi.org/10.1007/978-3-319-24474-7_20
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DOI: https://doi.org/10.1007/978-3-319-24474-7_20
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