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The extraction of association rules remains a crucial strategy in data analysis, particularly in the context of massive datasets. This method unveils complex relationships, correlations, and meaningful patterns within vast datasets, providing essential insights for decision-making and understanding behaviors. Our approach stands out with the use of clustering algorithms for intelligent data partitioning. This strategic choice establishes a robust foundation for efficient association rule extraction. By organizing the data specifically through clustering techniques before applying the extraction algorithm, we aim to optimize the relevance and significance of the discovered rules.

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Data Exploration by Unifying Clustering and Association Rule Mining

  • 会议论文

  • 发表日期：

    2025年

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  • Youssef Fakir,
  • Salim Khalil,
  • Hamid Garmani

摘要

The extraction of association rules remains a crucial strategy in data analysis, particularly in the context of massive datasets. This method unveils complex relationships, correlations, and meaningful patterns within vast datasets, providing essential insights for decision-making and understanding behaviors. Our approach stands out with the use of clustering algorithms for intelligent data partitioning. This strategic choice establishes a robust foundation for efficient association rule extraction. By organizing the data specifically through clustering techniques before applying the extraction algorithm, we aim to optimize the relevance and significance of the discovered rules.