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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Fournier-Viger, P., Yang, P., Lin, J.CW., Kiran, R.U. (2019). Discovering Stable Periodic-Frequent Patterns in Transactional Data. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_21
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