Frequent Pattern Discovery in Retail Transactions Using Improved FP-Growth with Adaptive Pruning
Keywords:
Frequent pattern mining, fp-growth, adaptive pruning, retail analytics, market basket analysis, association rules, data mining, transaction datasetsAbstract
Frequent pattern discovery remains a cornerstone technique in retail analytics, enabling retailers to extract actionable insights from transaction data. In this paper, we present an enhanced FP-Growth algorithm integrated with an adaptive pruning strategy tailored for large-scale retail transaction environments. The approach focuses on dynamically adjusting the pruning threshold based on transaction density and item frequency distribution, resulting in reduced tree complexity and faster pattern retrieval. Evaluated on contemporary datasets, the proposed method demonstrates significant improvements in computation time and memory usage while maintaining pattern accuracy. This study positions itself within the growing need for scalable, context-aware data mining solutions in real-time commerce environments.
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Copyright (c) 2026 Geoffrey Shaaban, Adriana Joan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
