A Temporal Adaptive Fuzzy Clustering Framework for Dynamic Behavioral Segmentation in E-Commerce

Abstract

Large volumes of behavioral data are generated by e-commerce platforms through customer browsing patterns, transaction histories, and product interactions. However, the complexity, noise, and temporal evolution of such behaviors are not adequately captured by traditional clustering methods. To address these limitations, an Adaptive Fuzzy Clustering for Behavioral Segmentation (AFCBS) framework is proposed. In this framework, temporal adaptation, robust preprocessing, and outlier handling are integrated to model evolving and overlapping behavioral patterns. At its core, a Fuzzy C-Means with Temporal Adaptation (FCM-TA) algorithm is introduced, in which temporal weighting is incorporated into the objective function so that dynamic and valid fuzzy memberships are maintained. The framework is evaluated on the UCI Online Retail dataset, where 488,000 cleaned transactions from 4,300 customers are analyzed. Comparative experiments are conducted against K-means, Gaussian Mixture Models, classical FCM, and hierarchical fuzzy clustering. Superior segmentation performance is achieved by AFCBS, as reflected in higher cohesion and separation (Silhouette = 0.46), lower fuzziness (Partition Entropy = 0.73), and stronger temporal consistency (TSI = 0.82). A simulated marketing scenario further indicates that a 19.4% increase in conversion rates can be obtained when AFCBS-based segmentation is used.