Observational analysis reveals strong product associations in transaction data, suggesting insights for marketing and inventory management.
Association Rule Mining is a key technique in data mining and machine learning for discovering hidden patterns and relationships in large datasets. It plays a crucial role in market basket analysis by uncovering associations among items frequently purchased together. This research focuses on applying the Apriori algorithm to transaction data from an online store to discover strong and meaningful associations among products. The study reveals key product combinations that reflect customer purchasing behavior, providing insights for targeted marketing, inventory optimization, and strategic decision-making. The experimental results demonstrate the effectiveness of Apriori in identifying actionable product pairings and bundles.
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Kumar et al. (2024) studied this question.
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