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Optimizing Data Exploration by Unifying Clustering and Association Rule Extraction
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Optimizing Data Exploration by Unifying Clustering and Association Rule Extraction
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Journal Article
Optimizing Data Exploration by Unifying Clustering and Association Rule Extraction
2025
Overview
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.
Publisher
Slovenian Society Informatika / Slovensko drustvo Informatika
Subject
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