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2023 | OriginalPaper | Buchkapitel

Discovery of Periodic Rare Correlated Patterns from Static Database

verfasst von : Upadhya K. Jyothi, B Dinesh Rao, M. Geetha, Harsh Kamlesh Vora

Erschienen in: Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering

Verlag: Springer Nature Singapore

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Abstract

Finding the associations among the itemsets and discovering the unknown or unexpected behavior are the major tasks of rare pattern mining. The support measure has the main contribution during the discovery of low support patterns. As the association of low support patterns may generate a bundle of spurious patterns, other measures are used to find the correlation between the itemsets. A generalization of frequent pattern mining called periodic frequent pattern mining (PFPM) is emerged as a promising field, focusing on the occurrence behavior of frequent patterns. On the contrary, the shape of occurrence in the case of rare pattern mining is not much studied. In this paper, a single scan algorithm called \( PRCPMiner\) is proposed to study the shape of occurrence of rare patterns. The proposed algorithm discovers periodic rare correlated patterns using different thresholds with respect to support, bond, and periodicity measures. The research shows the influence of these thresholds on the runtime performance for various datasets.

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Vorheriges Kapitel Virtual Reality in E-commerce: A Study
Nächstes Kapitel Performance and Application of Digital Forensic Tools: A Comparative Study
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Metadaten
Titel
Discovery of Periodic Rare Correlated Patterns from Static Database
verfasst von
Upadhya K. Jyothi
B Dinesh Rao
M. Geetha
Harsh Kamlesh Vora
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering

Print ISBN: 978-981-19-2224-4

Electronic ISBN: 978-981-19-2225-1

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-981-19-2225-1_56

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