Are your friends who they say they are?: data mining online identities
Crossroads - The Social Web
Fuzzy association rule mining for community crime pattern discovery
ACM SIGKDD Workshop on Intelligence and Security Informatics
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Crime profiling for the Arabic language using computational linguistic techniques
Information Processing and Management: an International Journal
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In this paper, we give comprehensive surveys of efficient and effective methods/techniques on data mining for crime data analysis. These techniques aim at finding the illegal activities of professional identity fraudsters based on knowledge discovered from their own histories. We also raise some problems of applied data mining in crime control and criminal suppression. It is known that detecting crime from data analysis can be difficult because daily activities of criminal generate large amounts of data and stem from various formats. In addition, the quality of data analysis depends greatly on background knowledge of analyst. A criminal can range from civil infractions such as illegal driving to terrorism mass murder such as the 9/11 attacks, therefore it is difficult to model the perfect algorithm to cover all of them. Finally, this paper proposes a guideline to overcome the problem.