Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

doi:10.1109/ACCESS.2023.3326419

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

2023-01-01

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DOI：10.1109/ACCESS.2023.3326419

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Periodic-frequent patterns are a vital class of regularities in a temporal database. Most previous studies followed the approach of finding these patterns by storing the temporal occurrence information of a pattern in a list. While this approach facilitates the existing algorithms to be practicable on sparse databases, it also makes them impracticable (or computationally expensive) on dense databases due to increased list sizes. A renowned concept in set theory is that the larger the set, the smaller its complement will be. Based on this conceptual fact, this paper explores the complements, redefines the periodic-frequent pattern and proposes an efficient depth-first search algorithm that finds all periodic-frequent patterns by storing only non-occurrence information of a pattern in a database. Experimental results on several databases demonstrate that our algorithm is efficient.

Keywords:

pattern mining

periodic patterns

set complements

temporal databases

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

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