[1] J. Xu Jennifer and Chen Hsinchun, “Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks”, Decision Support Systems Elsevier, pp. 473-487, 2004.
[2] L. Buczak Anna and M. Gifford Christopher, “Fuzzy Association Rule Mining for Community Crime Pattern Discovery”, ACM, 2010.
[3] Vadivel A. and G. Shaila S. , “Event Pattern Analysis and Prediction at Sentence Level using Neuro-Fuzzy Model for Crime Event Detection”, Pattern Anal Applic Springer, 2014.
[4] Zeleznikow John et al, “A Methodology for Constructing Decision Support Systems for Crime Detection”, Springer, pp. 823-829, 2005.
[5] J. Schreck Christopher et al, “Examining What Makes Violent Crime Victims Unique: Extending Statistical Methods for Studying Specialization to the Analysis of Crime Victims”, J Quant Criminol Springer, pp. 651-671, 2012.
[6] S. Albanese Jay, “The prediction and control of organized crime: a risk assessment instrument for targeting law enforcement efforts”, Trends in Organized Crime, pp. 4-29, 2001.
[7] Bogomolov Andrey et al, “Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data”, ACM, pp. 427-434, 2014.
[8] Zhang Chao et al, “Keeping Pace with Criminals: Designing Patrol Allocation against Adaptive Opportunistic Criminals”, International Foundation for Autonomous Agents and Multiagent Systems, pp. 1351-1359, 2015.
[9] Poolsappasit Nayot and Ray Indrakshi, “TowardsAchieving Personalized Privacy for Location-Based Services”, Transactions on data privacy, pp. 77-99, 2009.
[10] Keppensa Jeroen and Schafer Burkhard, “Knowledge based crime scenario modelling”, Expert Systems with Applications Elsevier, pp. 203-222, 2006.
[11] Adderley Richard and Bond John, “Predicting crime scene attendance”, Policing: An International Journal of Police Strategies & Management, Emerald Group Publishing Limited, pp. 292-305, 2008.
[12] M. Arietta Sean et al, “City Forensics: Using Visual Elements to Predict Non-Visual City Attributes”, IEEE transactions on visualization and computer graphics, pp. 2624 – 2633, 2014.
[13] Maurushat Alana et al, “Using ‘Big’ Metadata for Criminal Intelligence: Understanding Limitations and Appropriate Safeguards”, ACM, pp. 196-200, 2015.
[14] J. Xu Jennifer and Chen Hsinchun, “CrimeNet Explorer: A Framework for Criminal Network Knowledge Discovery”, ACM Transactions on Information Systems, pp. 201-226, 2005.
[15] Kadar Cristina and Pletikosa Cvijikj Irena, “City Watch: The Personalized Crime Prevention Assistant”, ACM, pp. 260-261, 2014.
[16] Yu Chung-Hsien et al, “Crime Forecasting Using Data Mining Techniques”, Proceedings 11th IEEE International Conference on Data Mining Workshops, pp. 779-786, 2011.
[17] Calders Toon and Verwer Sicco, “Three naive Bayes approaches for discrimination-free classification”, Data Min Knowl Disc Springer, pp. 277-292, 2010.
[18] Jaitman Laura and Machin Stephen, “Crime and immigration: new evidence from England and Wales”, IZA Journal of Migration Springer, pp. 2 – 19, 2013.
[19] Farrell Graham et al, “Debuts and legacies: the crime drop and the role of adolescence‑limited and persistent offending”, Crime Science a Springer Open Journal, pp. 4-16, 2015.
[20] Murray Joseph et al, “Childhood behaviour problems predict crime and violence in late adolescence: Brazilian and British birth cohort studies”, Soc Psychiatry PsychiatrEpidemiol Springer, pp. 579-589, 2015.
[21] Wan Lee Sang et al, “A Non supervised Learning Framework of Human Behavior Patterns Based on Sequential Actions”, IEEE transactions on knowledge and data engineering, pp. 479-492, 2010.
[22] J. Agarwal et al, “Crime analysis using k-means clustering”, International Journal of Computer Applications, pp 1 - 4, 2013.
[23] P. Gera, and R. Vohra, “Predicting Future Trends in City Crime Using Linear Regression”, IJCSMS, pp 58 – 64, 2014.
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