Anomalous Telecom Customer Behavior Detection and Clustering Analysis Based on ISP’s Operating Data

Mobile networks and smart phones have become ubiquitous in our daily life. Large amount of customer related telecom data from various sources are generated every day, from which diversified behavior patterns can be revealed, including some anomalous behaviors that are vicious. It becomes increasingly important to achieve both efficient and effective customer behavior analysis based on the telecom big data. In this paper, the Multi-faceted Telecom Customer Behavior Analysis (MTCBA) framework for anomalous telecom customer behavior detection and clustering analysis is proposed. In this framework, we further design the hierarchical Locality Sensitive Hashing-Local Outlier Factor (hierarchical LSH-LOF) scheme for suspicious customer detection, and the Autoencoders with Factorization Machines (FM-AE) structure for dimension reduction to achieve more efficient clustering. Hierarchical LSH-LOF is an improved algorithm of LOF, in which we design a hierarchical LSH process that selects the approximate k nearest neighbors from coarse to fine by gradually narrowing down the scope. Experiments show its superiority over KD-tree w.r.t searching speed. FM-AE exploits Factorization Machines for learning second order feature interactions, which we prove to be useful by designing comparative experiments with five dimension reduction algorithms. With the proposed MTCBA framework, efficient and effective telecom customer behavior analysis including anomalous customer behavior detection and clustering analysis is performed on the real world telecom operating data provided by one of the major Internet service providers (ISPs) in China. Meanwhile, interpretable clustering results of six clusters are obtained to provide valuable information for the precision marketing of telecom operators, criminal combating, and social credit system construction.