A combinatorial clustering method for sequential fraud detection
Yuqi Yu, Xin Wan, Guannan Liu, Hong Li, Peng Li, Hao Lin · 2017
Telecommunications fraud, a new type of crime, is showing a rising trend in recent years. However, research from data mining perspectives to detect such frauds is scarce, especially with the behavioral sequences considered. Though the call detail records (CDRs) in telecommunication is generally a snapshot, the history of a caller/callee can be treated as sequences. Indeed, the historical calling sequences can better indicate the fraudulent behaviors. Thus, clustering the time sequences provides a new way to identify fraudulent groups, and therefore the problem of clustering the multidimensional sequences has to be addressed. To that end, in this paper, a combinatorial clustering method for sequential data is presented to identify fraudulent groups and reveal the underlying behavioral patterns. Specifically, the historical CDR data is divided into multiple sequences with the time interval of an hour. Then, several key features are chosen, and K-means in combination with Dynamic Time Warping (DTW) is performed on the time series data with each individual feature. Finally, the individual feature clustering results are ensembled into the final result. Experiments on real world data set demonstrate the effectiveness of the method.