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@article{194914,
        author = {Dr John Mathew and T Satya Hanuman and V Ajay and Y Himabindu},
        title = {CUSTOMER SEGMENTATION AND MARKET BASKET ANALYSIS: MACHINE LEARNING CLUSTERING AND ASSOCIATION RULE MINING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8119-8123},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194914},
        abstract = {In today’s competitive business environment, understanding customer behaviour plays a crucial role in decision-making. Traditional data analysis techniques fail to extract meaningful patterns from large transactional datasets. This paper presents a machine learning-based approach for customer segmentation and market basket analysis using clustering and association rule mining techniques. Customer segmentation is performed using the K-Means clustering algorithm based on RFM (Recency, Frequency, Monetary) analysis to identify different customer groups. Market basket analysis is carried out using the Apriori algorithm to discover frequent item sets and association rules among purchased products. The proposed approach helps businesses identify valuable customers and understand product purchasing patterns, which can be used for targeted marketing and cross-selling strategies. Experimental results demonstrate that machine learning techniques provide efficient and interpretable insights for business intelligence applications.},
        keywords = {Customer Segmentation, Market Basket Analysis, Machine Learning, K-Means Clustering, Apriori Algorithm},
        month = {March},
        }