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GRIMP: A Genetic Algorithm for Compression‐Based Descriptive Pattern Mining.

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Title: GRIMP: A Genetic Algorithm for Compression‐Based Descriptive Pattern Mining.
Authors: Nawaz, M. Zohaib1,2 (AUTHOR), Nawaz, M. Saqib1 (AUTHOR), Fournier‐Viger, Philippe1 (AUTHOR) philfv@szu.edu.cn, Selmaoui‐Folcher, Nazha3 (AUTHOR)
Source: Expert Systems. May2025, Vol. 42 Issue 5, p1-20. 20p.
Subjects: Patterns (Mathematics), Genetic variation, Data mining, Genetic algorithms, Algorithms
Abstract: Traditional frequent pattern mining algorithms often report an overwhelming number of patterns in large datasets, many of which are redundant. To address this issue, Minimum Description Length (MDL)‐based methods have been employed, which use data compression to capture a smaller yet significant set of patterns. However, finding a good set of patterns according to MDL involves a very large search space, and current MDL‐based techniques often suffer from long runtimes and find suboptimal solutions. To discover better sets of patterns in less time, this paper introduces GRIMP (a Genetic algoRIthm for coMpression‐based descriptive Pattern mining), a novel framework that combines a genetic algorithm with MDL‐based pattern selection. Multiple genetic algorithm variants are explored within the GRIMP framework, and their effectiveness is compared using a large number of datasets. Experimental results demonstrate that GRIMP consistently outperforms previous methods by achieving higher compression ratios, generating more representative itemsets, and requiring less time. Additionally, the extracted patterns improve downstream classification tasks, highlighting the ability of GRIMP to find more representative patterns within the data. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems is the property of Wiley-Blackwell 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: Traditional frequent pattern mining algorithms often report an overwhelming number of patterns in large datasets, many of which are redundant. To address this issue, Minimum Description Length (MDL)‐based methods have been employed, which use data compression to capture a smaller yet significant set of patterns. However, finding a good set of patterns according to MDL involves a very large search space, and current MDL‐based techniques often suffer from long runtimes and find suboptimal solutions. To discover better sets of patterns in less time, this paper introduces GRIMP (a Genetic algoRIthm for coMpression‐based descriptive Pattern mining), a novel framework that combines a genetic algorithm with MDL‐based pattern selection. Multiple genetic algorithm variants are explored within the GRIMP framework, and their effectiveness is compared using a large number of datasets. Experimental results demonstrate that GRIMP consistently outperforms previous methods by achieving higher compression ratios, generating more representative itemsets, and requiring less time. Additionally, the extracted patterns improve downstream classification tasks, highlighting the ability of GRIMP to find more representative patterns within the data. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell 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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        Value: 10.1111/exsy.70033
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        Text: English
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      – SubjectFull: Genetic variation
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      – SubjectFull: Data mining
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      – SubjectFull: Genetic algorithms
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      – SubjectFull: Algorithms
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              M: 05
              Text: May2025
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