Keywords
data stream mining
behavioral clustering
Abstract
E-commerce platforms generate continuous clickstream data from product views, searches, cart additions, checkout attempts, payment redirects, session duration, and user-device interactions. These behavioral streams change rapidly during promotions, seasonal sales, recommendation updates, and traffic campaigns, making anomaly detection difficult for static clustering methods. This study proposes a habituation-controlled behavioral clustering method for real-time anomaly detection in e-commerce clickstream data. The method constructs evolving behavior clusters for normal user sessions and introduces a habituation-control factor to prevent repeated suspicious behaviors, such as bot browsing and abnormal checkout attempts, from being absorbed too quickly into normal clusters. A session-transition scoring module is further designed to measure deviations in browsing paths, cart behavior, device switching, and payment-entry frequency. Experiments are conducted on an e-commerce clickstream dataset containing 38.6 million user sessions, 412 million page-event records, 1.9 million product identifiers, and 86 behavioral indicators collected over 72 days. The dataset includes 2,740 annotated abnormal episodes, including bot-driven page traversal, abnormal cart stuffing, coupon abuse, repeated checkout failure, and coordinated traffic bursts. The proposed method reduces median detection delay from 13.6 minutes to 4.1 minutes compared with a conventional streaming k-means baseline. False alerts are controlled at 3.2 investigation cases per million sessions. The system processes 92,000 page events per second and maintains 8,460 active behavioral clusters during peak traffic. Cluster displacement remains below 0.22 normalized units during normal promotion-period traffic shifts. The results show that habituation-controlled clustering can improve real-time anomaly detection in high-volume, non-stationary clickstream data.
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Copyright (c) 2026 Li Wei, Tan Ming, Chen Jiahao (Author)