Chinese Journal of Computers   Full Text
Title	Mining Key Members of Crime Networks Based on Personality Trait Simulation Email Analysis System
Authors	QIAO Shao-Jie1) TANG Chang-Jie1) PENG Jing2) LIU Wei1) WEN Fen-Lian1) QIU Jiang-Tao1)
Address	1)(School of Computer Science, Sichuan University, Chengdu 610065) 2)(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871)
Year	2008
Issue	No.10(1795—1803)
Abstract &   Background	Abstract It is a new paradigm to apply data mining technologies to analyze the crime groups and terrorist social networks, there is little work being done on analyzing the communication behavior of criminal and terrorist groups. This paper designed a simulation email system based on personality trait dimensions, called MEP, to model the email users’ traffic behavior, proposed a new approach of computing the weight of each dimension in a personality trait vector by using personality trait judge matrix, and simulated the real-world email communication behavior based on normal distribution model satisfying users’ personality trait. This paper proposed a social network analysis based algorithm called CNKM (Crime Network Key Member mining) to mine key members of a crime group, and employed time-series analysis techniques to discover the email sending and receiving rules in order to detect the abnormal communication cases. The experimental results show the efficiency and usability of the simulation email analysis system, the average simulation error is less than 10%, and demonstrate that CNKM is efficient. Keywords data mining; personality trait; simulation; email analysis system; social network analysis Background This work is supported by the National Natural Science Foundation of China under grant No.60773169, the 11th Five Years Key Programs for Sci. & Tech. Development of China under grant No.2006BAI05A01, the Foundation of Innovation Software Engineering for Young People in Sichuan under grant Nos.2007AA0032 and 2007AA0028, and is also supported by Sichuan Youth Science and Technology Foundation under grant No.08ZG026-16. The project aims to help the law enforcement and intelligence agencies discover knowledge from crime networks in an efficient and effective manner. This project proposes a framework for crime data mining, which includes four main stages: Crime and terrorist prediction, crime and terrorist network creation, structure analysis, and network visualization. The authors have done research on crime and terrorist network analysis, mining key members of crime and terrorist networks and developing applications related to crime data mining. Currently, they have made some progress in analyzing the communication behavior among terrorist groups, and mining key members of crime networks by Gene Expression Programming (GEP). The existing modules introduced in this paper has been integrated into the Crime Miner system that is funded by the Foundation of Innovation Software Engineering for Young People in Sichuan, and the state-of-art work introduced in this paper is useful and practical in the crime data mining research area.