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PISRuleGrowth: mining periodic intra-sequential rules common to multiple sequences.

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Title: PISRuleGrowth: mining periodic intra-sequential rules common to multiple sequences.
Authors: Milenković, Nevena1 (AUTHOR) nevenamilenkovic2706@gmail.com, Delibašić, Boris1 (AUTHOR)
Source: Knowledge & Information Systems. Jul2025, Vol. 67 Issue 7, p6071-6114. 44p.
Subjects: Information storage & retrieval systems, Sequential pattern mining, Artificial intelligence, Image processing, Data mining
Abstract: Sequential rule mining remains an important research topic in data mining. Discovering conditioned events in large databases can provide beneficial insights. However, periodic intra-sequential mining has also emerged as an interesting topic, describing patterns that are simultaneously periodic within sequences across different sequences. These patterns have been proven especially useful in sales and marketing, aiding in targeting a group of customers based on their common periodic behavior. Motivated by previous works, we propose a new mining task to discover periodic sequential rules that appear in multiple sequences. The goal is to find causal temporal relationships in periodic intra-sequential patterns, producing more meaningful patterns while retaining the significance of the information they provide. We also emphasize that the periodicity constraint was not introduced in partially-ordered sequential and intra-sequential rules beforehand. In this paper, we propose the periodic intra-sequential RuleGrowth (PISRuleGrowth) algorithm, which is based on the pattern-growth approach and uses a map structure to remain efficient even on large databases. Extensive experiments on real datasets confirmed that the algorithm outperforms the existing algorithms in terms of speed and memory usage. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: PISRuleGrowth: mining periodic intra-sequential rules common to multiple sequences.
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  Data: <searchLink fieldCode="JN" term="%22Knowledge+%26+Information+Systems%22">Knowledge & Information Systems</searchLink>. Jul2025, Vol. 67 Issue 7, p6071-6114. 44p.
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  Data: Sequential rule mining remains an important research topic in data mining. Discovering conditioned events in large databases can provide beneficial insights. However, periodic intra-sequential mining has also emerged as an interesting topic, describing patterns that are simultaneously periodic within sequences across different sequences. These patterns have been proven especially useful in sales and marketing, aiding in targeting a group of customers based on their common periodic behavior. Motivated by previous works, we propose a new mining task to discover periodic sequential rules that appear in multiple sequences. The goal is to find causal temporal relationships in periodic intra-sequential patterns, producing more meaningful patterns while retaining the significance of the information they provide. We also emphasize that the periodicity constraint was not introduced in partially-ordered sequential and intra-sequential rules beforehand. In this paper, we propose the periodic intra-sequential RuleGrowth (PISRuleGrowth) algorithm, which is based on the pattern-growth approach and uses a map structure to remain efficient even on large databases. Extensive experiments on real datasets confirmed that the algorithm outperforms the existing algorithms in terms of speed and memory usage. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Sequential pattern mining
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              Text: Jul2025
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