Modus Operandi Modelling of Group Offending: A Data-Mining Case Study

International Journal of Police Science and Management

Volumn 5, Issue 4, 2003, Pages 265-276

  Adderley, Richard ^a   Musgrove, Peter ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 27544437794     PISSN: 14613557     EISSN: 14781603     Source Type: Journal    
DOI: 10.1350/ijps.5.4.265.24933     Document Type: Article

References (19)

1. Adderley R. Musgrove P.B. (1999). Data mining at the West Midlands Police: A study of bogus official burglaries. In Applications and Innovations in Intelligent Systems VII, Proceedings of ES99, 19th SGES International Conference on KBS + AI, Cambridge: December, pp. 191–203. London: Springer Verlag.
2. Adderley R. Musgrove P.B. (2001a). General review of police crime recording and investigation systems. A user's view. Policing: An International Journal of Police Strategies and Management, 24(1), 100–114.
3. Adderley R. Musgrove P.B. (2001b). Data mining case study: Modelling the behaviour of offenders who commit serious sexual assaults (ACM Special Interest Group on Knowledge Discovery and Data Mining). Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco. New York: Association for Computing Machinery Inc.
4. Adriaans P. Zantige D. (1996). Data Mining. New York: Addison-Wesley.
5. Brantingham P.L. Brantingham P.J. (1991). Notes on the geometry of crime. In Brantingham P.L. Brantingham P.J., (Eds.), Environmental Criminology. USA: Waveland Press Inc.
6. Broomhead D.S. Lowe D. (1998). Multivariable functional interpolation and adaptive networks. Complex Systems, 2, 321–355.
7. Chapman P. Clinton J. Kerber R. Khabaza T. Reinartz T. Shearer C. Wirth R. (2000). CRISP-DM 1.0 Step-by-step data mining guide. USA: SPSS Inc. CRISPWP-0800 2000.
8. Charles J. (1998, Jan/Feb). AI and law enforcement. IEEE Intelligent Systems. 77–80.
9. Clarke R.V. Felson M. (1993). Introduction: Criminology, routine activity, and rational choice. In Clarke R.V. Felson M. (Eds.), Routine activity and rational choice: Advances in criminological theory. Vol. 5. New Jersey, USA: Transaction Publishers.
10. Cohen L.E. Felson M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608.
11. Coles N. (2001). It's not what you know — It's who you know that counts. Analysing serious crime groups as social networks. British Journal of Criminology, 44, 580–594.
12. Felson M. (1992). Routine activities and crime prevention: Armchair concepts and practical action. Studies on Crime and Crime Prevention, 1, 30–34.
13. Kohonen T. (1982). Self organising formation of topologically correct feature maps, Biological Cybernetics, 43(1), 59–69.
14. Lucas R. (1986). An expert system to detect burglars using a logic language and a relational database. Paper presented at the 5th British National Conference on Databases, Canterbury.
15. Reiss A.J. (1988). Co-offending and criminal careers. In Tonry M. Morris N., (Eds.), Crime and justice, volume 10: An annual review of research (pp. 117–170). Chicago: University of Chicago Press.
16. Rhodes W.M. Conly C. (1991). The criminal commute: A theoretical perspective in environmental criminology. USA: Waveland Press Inc.
17. Ripley B.D. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.
18. Swingler K. (1996). Applying neural networks. San Francisco: Morgan Kaufman Publishers.
19. Weiss S.M. Indurkhya N. (1998). Predictive data mining: A practical guide. San Francisco, USA: Morgan Kaufman Publishers Inc.