Customer Segmentation and Classification Using K-Modes Clustering with Ensemble Learning

In the contemporary landscape of business intelligence and market analysis, customer segmentation serves as a pivotal tool for understanding consumer behavior and preferences. This paper delves into the application of advanced machine learning techniques, specifically K-Modes clustering and ensemble learning with AdaBoost, for the purpose of customer segmentation and classification. The utilization of K-Modes clustering, an extension of the K-Means algorithm tailored for categorical data, facilitates the identification of distinct groups within a heterogeneous customer base. By incorporating categorical variables, K-Modes accommodates the inherent diversity in customer attributes such as demographic information, purchase history, and product preferences. Furthermore, this research integrates ensemble learning techniques, particularly AdaBoost, to enhance the accuracy and robustness of the segmentation process. Through a comprehensive empirical analysis, conducted on a real-world dataset sourced from Kaggle, the proposed methodology demonstrates superior performance compared to traditional clustering approaches. The experimental results showcase the effectiveness of K-Modes clustering combined with AdaBoost ensemble learning in accurately segmenting customers into meaningful groups, thereby enabling businesses to gain deeper insights into consumer behavior and preferences.