Machine Learning-Based Customer Segmentation and Behavioral Analysis Using K-Means Clustering
Keywords:
market basket analysis, apriori algorithm, association rules, product recommendations, data miningAbstract
The rapid growth of transactional data in retail and e-commerce has created opportunities to understand customer purchasing behavior through Market Basket Analysis (MBA). This study applies the Apriori algorithm to identify product association patterns within transactional databases and evaluates the effectiveness of including product category parameters to enhance product package recommendations. A quantitative approach with an applied experimental method is used to systematically process and analyze transactional data. The study involves data preprocessing, application of the Apriori algorithm to generate frequent itemsets and association rules, and visualization of the results. Findings indicate that the algorithm successfully discovers frequently co-purchased product combinations, and the inclusion of product categories improves the relevance and quality of the resulting recommendations. This research provides practical benefits for businesses, such as guiding cross-selling strategies, optimizing inventory management, and enhancing customer satisfaction. Additionally, it contributes to the theoretical development of data mining applications in retail. The results suggest that leveraging association rules with enhanced parameters can support more effective marketing strategies and evidence-based decision-making in dynamic transactional environments.
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