An Efficient Data Mining Framework for Customer Behavior Analysis in E-Commerce Applications

Abstract

E-commerce platforms generate large volumes of transaction, clickstream, session, and contextual data, but these records become valuable only when they are converted into interpretable customer-behavior patterns. This methodology paper proposes an efficient data mining framework that integrates preprocessing, behavioral feature construction, customer segmentation, association-pattern discovery, and purchase-intention classification within a single analytical pipeline. The framework is designed to combine descriptive and predictive analysis: clustering identifies behaviorally similar customer groups, association rules reveal co-occurring actions or product interests, and supervised classification estimates whether an active session is likely to end in a purchase. A 2018 public benchmark, the Online Shoppers Purchasing Intention Dataset, is used to define the validation setting. It contains 12,330 independent sessions, with 1,908 purchase sessions and 10,422 non-purchase sessions, making class imbalance an important methodological consideration. Efficiency is improved through early data reduction, selective feature engineering, standardized transformation, and evaluation metrics that emphasize minority-class detection rather than raw accuracy alone. The proposed approach provides a reproducible structure for e-commerce analytics and supports customer segmentation, personalized offers, retention planning, and website optimization without depending on a single mining technique.