Optimizing Data Exploration by Unifying Clustering and Association Rule Extraction

Abstract

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 through the use of clustering algorithms for intelligent data partitioning. This strategic choice establishes a robust foundation for efficient association rule extraction. By organizing 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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Authors

  • Youssef Fakir Sultan Moulay Slimane University
  • Salim Khalil
  • Hamid Garmani
  • Mohamed Fakir

DOI:

https://doi.org/10.31449/inf.v49i18.5964

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Published

04/11/2025

How to Cite

Fakir, Y., Khalil, S., Garmani, H., & Fakir, M. (2025). Optimizing Data Exploration by Unifying Clustering and Association Rule Extraction. Informatica, 49(18). https://doi.org/10.31449/inf.v49i18.5964

Issue

Vol. 49 No. 18 (2025): Online-only issue

Section

Online-only

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