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<p>Frequent Pattern Mining (FPM) is a fundamental data mining task that identifies associations and patterns within datasets. We explore different techniques by reviewing prominent algorithms like Apriori, EClaT, FP-Growth, FIN, PrePost+, Pascal and LCMFreq. We categorize these approaches based on their candidate generation, data representation, and computational strategies, ranging from tree-based methods to pattern growth techniques. Numerical simulations are conducted on real-world datasets to compare their performance in terms of runtime, memory consumption, and scalability across different support thresholds.</p>

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Comparative Analysis of Frequent Pattern Mining Algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月18日

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  • Petru Kallay,
  • Tudor Dan Mihoc

摘要

Frequent Pattern Mining (FPM) is a fundamental data mining task that identifies associations and patterns within datasets. We explore different techniques by reviewing prominent algorithms like Apriori, EClaT, FP-Growth, FIN, PrePost+, Pascal and LCMFreq. We categorize these approaches based on their candidate generation, data representation, and computational strategies, ranging from tree-based methods to pattern growth techniques. Numerical simulations are conducted on real-world datasets to compare their performance in terms of runtime, memory consumption, and scalability across different support thresholds.