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@article{177017,
        author = {Raj Vardhan and Rahul Kumar and Suraj Nikhil and Suvam Chakraborty and Elaiyaraja P},
        title = {Customer Segmentation Using Machine Learning: A Credit Card Usage Clustering Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6-8},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177017},
        abstract = {This study applies unsupervised machine learning techniques to segment credit card users based on 18 behavioral variables. Using clustering algorithms like K-Means, Agglomerative Clustering, and Gaussian Mixture Models, we identify distinct customer groups. Principal Component Analysis (PCA) enhances clustering performance and visualization. The resulting segments—such as Big Spenders, Average Users, and High Riskers—offer actionable insights for marketing and risk management. Our approach demonstrates the potential of data-driven segmentation in financial analytics.},
        keywords = {Behavioral Segmentation, Unsupervised Profiling, Cluster Interpretability, Dimensionality Reduction Analytics, Customer Microtargeting, PCA-Enhanced Clustering, Financial Data Stratification},
        month = {April},
        }