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2019 | OriginalPaper | Buchkapitel

Discovering Stable Periodic-Frequent Patterns in Transactional Data

verfasst von : Philippe Fournier-Viger, Peng Yang, Jerry Chun-Wei Lin, Rage Uday Kiran

Erschienen in: Advances and Trends in Artificial Intelligence. From Theory to Practice

Verlag: Springer International Publishing

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Abstract

Periodic-frequent patterns are sets of items (values) that periodically appear in a sequence of transactions. The periodicity of a pattern is measured by counting the number of times that its periods (the interval between two successive occurrences of the patterns) are greater than a user-defined maxPer threshold. However, an important limitation of this model is that it can find many patterns having a periodicity that vary widely due to the strict maxPer constraint. But finding stable patterns is desirable for many applications as they are more predictable than unstable patterns. This paper addresses this limitation by proposing to discover a novel type of periodic-frequent patterns in transactional databases, called Stable Periodic-frequent Pattern (SPP), which are patterns having a stable periodicity, and a pattern-growth algorithm named SPP-growth to discover all SPP. An experimental evaluation on four datasets shows that SPP-growth is efficient and can find insightful patterns that are not found by traditional algorithms.

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Vorheriges Kapitel An Efficient Algorithm for Deriving Frequent Itemsets from Lossless Condensed Representation

Nächstes Kapitel Graphical Event Model Learning and Verification for Security Assessment

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Titel: Discovering Stable Periodic-Frequent Patterns in Transactional Data
verfasst von: Philippe Fournier-Viger
Peng Yang
Jerry Chun-Wei Lin
Rage Uday Kiran
Verlag: Springer International Publishing
Buch: Advances and Trends in Artificial Intelligence. From Theory to Practice
Print ISBN: 978-3-030-22998-6

Electronic ISBN: 978-3-030-22999-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-22999-3_21

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