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GRIMP: A Genetic Algorithm for Compression-Based Descriptive Pattern Mining
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DOI：10.1111/exsy.70033.png)
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Abstract
En 中文
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.
Keywords:
genetic algorithm
minimum description length
pattern mining data compression