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Decision support systems for police: Lessons from the application of data mining techniques to “soft” forensic evidence

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The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” evidence such as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems.

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  • Aamodt A. (1990). Knowledge-intensive Case-based Reasoning and Sustained Learning. In Aiello, L. (ed.) Proceedings of the 9th European Conference on Artificial Intelligence, ECAI-90, Stockholm, August, 6–10, 1–6. Pitman Publishing: London

    Google Scholar 

  • Aamodt, A. (1994). Explanation Driven Case Based Reasoning. In Aamodt, A., Wess, S., Althoff, K. and Richter, M. (eds.), Topics in Case Based Reasoning. Springer Verlag: 1848 Berlin, 274–288

  • Aamodt, A. (1995). Knowledge Acquisition and Learning by Experience – The Role of Case Specific Knowledge. In Kodratoff, and Tecuci (eds.), Machine Learning and Knowledge Acquisition, 197–245, Academic Press Ltd, ISBN 0-12-685120-4

  • Adderley, R. and Musgrove, P. B. (1999). Data Mining at the West Midlands Police: A Study of Bogus Official Burglaries. BCS Special Group Expert Systems, ES99, 191–203. Springer-Verlag: London

  • Adderley, R. and Musgrove P. B. (2001) Data Mining Case Study: Modeling the Behavior of Offenders who Commit Serious Sexual Assaults. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 26–29, 215–220. ACM: San Francisco, CA, USA

  • Aggarwal, C. C. (2003). Towards Systematic Design of Distance Functions for Data Mining Applications. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Washington D.C, USA, 9–18. ACM Press: New York, USA

  • Agrawal, R. and Srikant, R. (1994). Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Data Bases, 487–499. Santiago, Chile

  • Agrawal, R. and Srikant, R. (1995). Mining Sequential Patterns. In Proceedings of the International Conference on Data Engineering (ICDE), Taipei, Taiwan

  • Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26–28, ACM Press, 207–216

  • Aha, D. (1992). Tolerating Noisy, Irrelevant and Novel Attributes in Instance-based Learning Algorithms. International Journal of Man–Machine Studies. 36(2):267–287

    Article  Google Scholar 

  • Ahmed, S., Cohen, F., and Peng, P. (2003). Strategies for Partitioning Data in Association Rule Mining. In Proceedings AI-2003, 23rd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Dec 2003, Springer: Cambridge, 127–140

  • Aitken, C. (1995). Statistics and the Evaluation of Evidence for Forensic Scientists. John Wiley and Sons: Chichester, UK

    MATH  Google Scholar 

  • Allen, J. F. (1991). Temporal Reasoning and Planning. In Allen, J. F. (ed.) Reasoning About Plans, 8–33. Morgan Kaufmann: San Mateo

    Google Scholar 

  • Barker, M. (2000). The Criminal Range of Small-Town Burglars. In Canter D. and Alison L. (eds.), Profiling Property Crimes. Ashgate Publishing Co

  • Batagelj, V. and Mrvar, A. (2003). Pajek – Analysis and Visualization of Large Networks. In Junger, M. and Mutzel, P. (eds.), Graph Drawing Software, 77–103. Springer (series Mathematics and Visualization): Berlin, ISBN 3-540-00881-0

  • Bishop, C. (1995). Neural Networks for Pattern Recognition. Clarendon Press: Oxford

    Google Scholar 

  • Bowers, K., Newton, M., and Nutter, R. (2001). A GIS-linked Database for Monitoring Repeat Domestic Burglary. In Hirschfield, A. and Bowers, K. (eds.) Mapping and Analysing Crime Data – Lessons from Research and Practice, 120–137. Taylor and Francis: London, New York

    Google Scholar 

  • Brantingham, P. L. and Brantingham, P. J. (1981). Notes on the Geometry of Crime. In Brantingham, P. J. and Brantingham, P. L. (eds.) Environmental Criminology, 27–54. Waveland Press Inc.: Prospect Heights IL

    Google Scholar 

  • Canter, D. (2000). Offender Profiling and Criminal Differentiation. Legal and Criminological Psychology 5: 23–46

    Article  Google Scholar 

  • Canter, D. and Alison, L. (2000). Profiling Property Crimes. In Canter, D. and Alison, L. (eds.), Profiling Property Crimes, 1–31. Ashgate Publishing Ltd

  • Carlin, J. B. and Louis, T. A. (2000). Bayes and Empirical Bayes. Methods for Data Analysis, 2nd edn. Chapman and Hall: New York

    MATH  Google Scholar 

  • Canter, D., Coffey, T., Huntley, M., and Missen, C. (2000). Predicting Serial Killers’ Home Base Using a Decision Support System. Journal of Quantitative Criminology 16(4): 457–478

    Article  Google Scholar 

  • Chen, H. and Lynch, K. J. (1992). Automatic Construction of Networks of Concepts Characterising Document Databases. IEEE Transactions on Systems Sept/Oct, 885–902

  • Chen, H., Zeng, D., Atabakhsh, H., Wyzga, W., and Schroeder, J. (2003a). COPLINK Managing Law Enforcement Data and Knowledge. Communications of the ACM 46(1): 28–34

    Article  Google Scholar 

  • Chen, H., Schroeder, J., Hauck, R., Ridgeway, L., Atabakhsh, H., Gupta, H., Boarman, C., Rasmussen, K., and Clements, A. (2003b). COPLINK Connect: Information and Knowledge Management for Law Enforcement. Decision Support Systems (DSS), Special Issue “Digital Government: technologies and practices’ 34(3): 271–285

  • Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., and Chau, M. (2004). Crime Data Mining: A General Framework and Some Examples. IEEE Computer 37(4)

  • Chklovski, T. (2003). Learner: A System for Acquiring Commonsense Knowledge by Analogy. In The Proceedings of the International Conference on Knowledge Capture. Florida, USA, October 23–25 2003, 4–12. ACM Press: New York, USA

  • Clementine SPSS Clementine home page. [Online]. Available from: [Accessed 2004]

  • COPS. Community Oriented Policing Services home page. [Online]. Available from: [Accessed 2004]

  • Corcoran, J., Wilson, I. D., Lewis, O. M., and Ware, J. A. (2001). Data Clustering and Rule Abduction to Facilitate Crime Hot Spot Prediction. Lecturer Notes in Computer Science 2206: 807–822

    Article  Google Scholar 

  • Corcoran, J., Wilson, I. D., and Ware J. A. (2003). Predicting the Geo-Temporal Variations of Crime and Disorder. International Journal of Forecasting 19(4): 623–634, Elsevier

    Google Scholar 

  • Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J. (1999). Probabilistic Networks and Expert Systems. Springer-Verlag: New York

  • Coxon, A. P. M. (1982). The Users’ Guide to Multidimensional Scaling. Heinemann: London

  • Crime and Disorder Act (1998). ISBN 0 10 543798 0

  • Crimestat Manual (2004). Chapter 9 – Journey to Crime Estimation. [Online]. Available: [2004, May 6]

  • Davies, P. M. and Coxon, A. P. M. (1982). Key Texts in Multidimensional Scaling. Heinemann, London

    Google Scholar 

  • Deltour, A., (2001). Tertius Technical Report CSTR-01-001 Department of Computer Science, University of Bristol, September 2001. [Online]. Available from: = 1000568 [Accessed 2004, May 20]

  • Dodd, T., Nicholas, S., Povey, D., and Walker, A. (2004). Home Office Statistical Bulletin, Crime in England and Wales 2003/2004. Research, Development and Statistics Directorate, Crown

  • Dykes, J. A. and Mountain, D. M. (2003). Seeking Structure in Records of Spatio-Temporal Behaviour: Visualization Issues, Efforts and Applications. Computational Statistics and Data Analysis 43: 581–603

    Article  MathSciNet  MATH  Google Scholar 

  • Elias, P. (1995). Social Class and the Standard Occupational Classification. In Rose, D. (ed.), A Report on Phase 1 of the ESRC Review of Social Classifications. ESRC: Swindon

  • Everitt, B. (1974). Cluster Analysis. Heinemann Educational books, Ltd: London

    Google Scholar 

  • Everitt, B. S. and Dunn, G. (1991). Applying Multivariate Data Analysis. Edward Arnold

  • Ewart, B. W. and Oatley, G. C. (2003). Applying the Concept of Revictimization: Using Burglars’ Behaviour to Predict Houses at Risk of Future Victimization. International Journal of Police Science and Management 5(2): 69–85

    Google Scholar 

  • Ewart B. W. and Oatley G. C. (2005) Dimensions of Burglary: A Disaggregated Approach. Paper presented to the 15th Conference of the European Association of Psychology and Law, June 30th–1st July, Vilnius, Lithunia

  • Ewart, B. W., Inglis, P., Wilbert, M. N., and Hill, I. (1997). An Analysis of Time Intervals of Victimisation. Forensic Update 50: 4–9

    Google Scholar 

  • Ewart, B. W., Oatley, G. C., and Burn, K. (2005). Matching Crimes Using Burglars’ Modus Operandi: A Test of Three Models. International Journal of Police Science and Management 7(3): 160–174

    Article  Google Scholar 

  • Farrington, D. P. and Lambert, S. (2000) Statistical Approaches to Offender Profiling. In Canter D. and Alison L. (eds.), Profiling Property Crimes. Ashgate Publishing Co

  • Fayyad, U. M. and Stolorz, P. (1997). Data Mining And KDD: Promises and Challenges. Future Generation Computer Systems 13: 99–115

    Article  Google Scholar 

  • Fayyad, U. M, Piatetsky-Shapiro, G., and Smyth, P. (1996). The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communications ACM 39(11): 27–41

    Article  Google Scholar 

  • Gebhardt, F. et al. (1997). Reasoning with Complex Cases. Kluwer Academic Publishers: Boston Massachusetts USA

    MATH  Google Scholar 

  • Gigerenzer, G. (1991). How to Make Cognitive Illusion Disappear: Beyond Heuristics and Biases. In Stroebe, W. and Hewstone, M. (eds.), European Review of Social Psychology, Vol 2. John Wiley and Sons: London

  • Goldberg, H. G. and Wong, R. W. H. (1998). Restructuring Transactional Data for Link Analysis in the FinCEN AI System. In Jensen, D. and Goldberg, H. (eds.), Artificial Intelligence and Link Analysis. Papers from the AAAI Fall Symposium. Orlando, Florida, Tech Report FS-98-01

  • Green, E. J., Booth, C. E., and Biderman, M. D. (1976). Cluster Analysis of Burglary M/O’s. Journal of Police Science and Administration 4: 382–388

    Google Scholar 

  • Gupta, K. M. and Montazemi, A. R. (1997). Empirical Evaluation of Retrieval in Case-Based Reasoning Systems Using Modified Cosine Matching Function. Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 27(5): 601–612

    Article  Google Scholar 

  • Han, J., Kamber, M. (2001). Data Mining: Concepts and Techniques. Morgan Kaufmann: San Francisco, CA

    Google Scholar 

  • Hastie, T., Tibshirani, R., and Friedman, J. (2003). The Elements of Statistical Learning. Springer-Verlag: Canada

    Google Scholar 

  • Hauck, R., Atabakhsh, H., Onguasith, P., Gupta, H., and Chen, H. (2002). Using Coplink to Analyse Criminal-Justice Data. IEEE Computer 35: 30–37

    Google Scholar 

  • Hirschfield, A. (2001). Decision Support in Crime Prevention: Data Analysis, Policy Evaluation and GIS. In Hirschfield, A. and Bowers, K. (eds.), Mapping and Analysing Crime Data – Lessons from Research and Practice, 237–269. Taylor and Francis: London, New York

    Google Scholar 

  • Horn, R. D, Birdwell, J. D., and Leedy, L. W. (1997). Link Discovery Tool. Proceedings of the Counterdrug Technology Assessment Center’s ONDCP/CTAC International Symposium Chicago, IL. August 18–22, 1997

  • HUGIN. Hugin Expert Home Page. [Online]. Available from: [Accessed 2005]

  • Hunter, A. (2000). Feature Selection Using Probabilistic Neural Networks. In Neural Computing and Applications Vol. 9, 124–132. Springer-Verlag: London

  • Hunter, A., Kennedy, L., Henry, J., and Ferguson, R. I. (2000). Applications of Neural Networks and Sensitivity Analysis to Improved Prediction of Trauma Survival. Computer Methods and Algorithms in Biomedicine 62: 11–19

    Article  Google Scholar 

  • I2. Investigative Analysis Software home page. [Online]. Available from: [Accessed 2004]

  • Jenson, F. V. (1996). An Introduction to Bayesian Networks. UCL Press

  • Johnson, S. D. and Bowers, K. J. (2004). The Stability of Space-time Clusters of Burglary. The British Journal of Criminology 44(1): 55–65

    Article  Google Scholar 

  • Kadane, J. B. and Schum, D. A. (1996). A Probabilistic Analysis of the Sacco and Vanzetti Evidence. John Wiley and Sons

  • Kahneman, D. and Tversky, A. (1973). On the Psychology of Prediction. Psychological Review 80: 237–251

    Article  Google Scholar 

  • Kleinbaum, D. G. (1996). Statistics in the Health Sciences: Survival Analysis. Springer-Verlag: New York

    Google Scholar 

  • Krause, P. and Clarke, D. (1993). Uncertain Reasoning: An Artificial Intelligence Approach. Intellect Books

  • Leary, R. M. (2001). Evaluation of the Impact of the FLINTS Software System in West Midlands and Elsewhere. Home Office Policing & Reducing Crime Unit: Home Office London

    Google Scholar 

  • Leary, R. M. (2002). The Role of the National Intelligence Model and “FLINTS’ in Improving Police Performance.

  • Levine, N. (2002). CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations (v 2.0). Ned Levine & Associates/National Institute of Justice: Houston, TX/Washington, DC

  • Leary, R. M. (2003). New Intelligence of the 21st Century: How Smart is it? Forensic Technology News November: 6, 2003

  • Mena, J. (2003). Investigative Data Mining for Security and Criminal Detection. Butterworth–Heinemann, ISBN 0-7506-7613-2

  • Merry, S. (2000).Crime Analysis: Principles for Analysing Everyday Serial Crime. In Canter, D. and Alison, L. (eds.), Profiling Property Crimes, 297–318. Ashgate Publishing Ltd

  • Merry, S. and Harsent, L. (2000). Intruders, Pilferers, Raiders and Invaders: The Interpersonal Dimension of Burglary. In Canter, D. and Alison, L. (eds.), Profiling Property Crimes, 31–57. Ashgate Publishing Ltd

  • Mooney, R. J., Melville, P., Rupert Tang, L.P., Shavlik, J., Dutra, I., Page, D., and Costa, V. S. (2004). Inductive Logic Programming for Link Discovery. In Kargupta, H., Joshi, A., Sivajumar, K., and Yesha, Y. (eds.), Data Mining: Next Generation Challenges and Future Directions. AAAI Press

  • Newell, A. and Simon, H. A. (1972). Human Problem Solving. Prentice-Hall: Englewood Cliffs, NJ

    Google Scholar 

  • Noy, N. F., Grosso, W., and Musen, M. A. (2000). Knowledge-Acquisition Interfaces for Domain Experts: An Empirical Evaluation of Protege-2000. Twelfth International Conference on Software Engineering and Knowledge Engineering (SEKE2000), Chicago, IL

  • Oatley, G. C. (2004). Case-based Reasoning (chapter). In Addison, D., and MacIntyre, J., (eds.), Intelligent Computing Techniques: A Review. Springer-Verlag, ISBN: 1-85233-585-8

  • Oatley, G. C. and Ewart, B. W. (2002). Constructing a Bayesian Belief Network to determine the likelihood of burglary. In Proceedings of the Fifth International Conference on Forensic Statistics (ICFS5), Isola di San Servolo, Venice, Italy, August 30 – September 2, 2002

  • Oatley, G. C. and Ewart, B. W. (2003). Crimes Analysis Software: “Pins in Maps”, Clustering and Bayes Net Prediction. Expert Systems with Applications 25(4): 569–588

    Article  Google Scholar 

  • Oatley, G. C. and Ewart, B. W. (2005). The Meaning of Links. In Nelson, D., Stirk, S., Edwards, H., and McGarry, K. (eds.), Data Mining and Knowledge Discovery in Databases Workshop, 22nd British National Conference on Databases, Vol. 2, 68–76. Bncod 22, July 5–7, 2005, Proceedings (Lecture Notes in Computer Science), Springer-Verlag Berlin and Heidelberg GmbH & Co. K, Vol. 2, pp. 68–76

  • Oatley, G. C., Tait, J., and MacIntyre, J. A. (1998). Case-Based Reasoning Tool for Vibration Analysis. In Milne, R., Macintosh, A., and Bramer, M. (eds.), Proceedings of the 18th Annual International Conference of the British Computer Specialist Group on Expert Systems (ES’98) – Applications and Innovations in Expert Systems VI, December 14–16, Springer, BCS Conference Series: Cambridge, 132–146

  • Oatley, G. C., MacIntyre, J., Ewart, B. W., and Mugambi, E. (2002). SMART Software for Decision Makers KDD Experience. Knowledge Based Systems 15: 323–333

    Article  Google Scholar 

  • Oskamp, S. (1965). Overconfidence in Case Study Judgements. Journal of Consulting Psychology 29: 261–265

    Article  Google Scholar 

  • Pastra, K., Saggion, H., and Wilks, Y. (2003). Intelligent Indexing of Crime-Scene Photographs. IEEE Intelligent Systems, Special Issue on “Advances In Natural Language Processing’ 18(1): 55–61

  • Patterson, D. W. (1998). Artificial Neural Networks: Theory and Applications. Prentice Hall (Sd), ISBN: 0132953536

  • Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc

  • Pearl J. (2000) Causality: Models, Reasoning, and Inference. Cambridge University Press, UK

    MATH  Google Scholar 

  • Pease, K. (1998). Repeat Victimisation: Taking Stock. Police Research Group, Crime Detection and Prevention Series, Paper 90. London, Crown Copyright

  • Pease, K. (2001) What to do about it? Lets turn off our minds and GIS. In Hirschfield, A. and Bowers, K. (eds.) Mapping and Analysing Crime Data – Lessons from Research and Practice. pp. 225–237. Taylor and Francis: London, New York

    Google Scholar 

  • Peuquet, D. J. and Duan, N. (1995). An Event-based Spatiotemporal Data Model (ESTDM) for Temporal Analysis of Geographical Data. International Journal of Geographical Information Systems 9(1): 7–24

    Google Scholar 

  • Peuquet, D. J. and Wentz, E. (1994). An Approach for TimedBasedSpatial Analysis of Spatio-Temporal Data. Advances in GIS Research Proceedings Vol. 1, 489–504

  • PITO (2004). PITO Core Data Model. [Online]. Available from: [Accessed 2006, Jan 20]

  • Polvi, N., Looman, T., Humphries, C., and Pease, K. (1991). The Time Course of Repeat Burglary Victimization. British Journal of Criminology 31(4): 411–414

    Google Scholar 

  • Protg. Homepage of Protege. Available: [2004, April 10]

  • Quinlan, R. (1998). C5.O: An Informal Tutorial. Rule Quest

  • Raafat, H., Yang, Z., and Gauthier, D. (1994). Relational Spatial Topologies for Historical Geographical Informations. International Journal of Geographical Information Systems 8(8): 163–173

    Google Scholar 

  • Ratcliffe, J. H. (2002). Aoristic Signatures and the Spatio-temporal Analysis of High Volume Crime Patterns. Journal of Quantitative Criminology 18(1): 23–43

    Google Scholar 

  • Ratcliffe, J. H. A. and McCullagh, M. J. (2001). Crime, Repeat Victimisation and GIS. In Hirschfield, A., Bowers, K. (eds.) Mapping and Analysing Crime Data – Lessons from Research and Practice. pp. 61–93. Taylor and Francis: London, New York

    Google Scholar 

  • Ribaux, O. and Margot, P. (1999). Inference Structures for Crime Analysis and Intelligence: The Example of Burglary Using Forensic Science Data. Forensic Science International 100: 193–210

    Article  Google Scholar 

  • Ribaux, O. and Margot, P. (2003). Case Based Reasoning in Criminal Intelligence using Forensic Case Data. Science and Justice. 43(3): 135–143

    Article  Google Scholar 

  • Roiger, R. J. and Geatz, M. W. (2003). Data Mining, a Tutorial-Based Primer. Pearson Education Inc, USA

    Google Scholar 

  • Rossmo, D. K. (1993). Multivariate Spatial Profiles as a Tool in Crime Investigation. In Block, C. R. and Dabdoub, M. (eds.), Proceedings of the Workshop on Crime Analysis through Computer Mapping. Illinois Criminal Justice Information Authority and Loyola University Sociology Department: Chicargo. Library of Congress HV7936.C88 W67

  • Rossmo, D. K. (1995). Overview: Multivariate Spatial Profiles as a Tool in Crime Investigation. In Block, C. R., Dabdoub, M., and Fregly, S. (eds.) Crime Analysis through Computer Mapping. pp. 65–97. Police Executive Research Forum: Washington, DC

    Google Scholar 

  • Rossmo, D. K. (1997). Geographic Profiling. In Jackson, J. L. and Bekerian, D. A. (eds.), Offender Profiling – Theory, Research and Practice, 159–177. John Wiley and Sons

  • Salton, G. and Buckley, C. (1988). Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24(5): 513–523

    Article  Google Scholar 

  • Schum, D. A. (1994). The Evidential Foundation of Probabilistic Reasoning. John Wiley and Sons

  • Schum, D. and Tillers, P. (1991). Marshalling Evidence for Adversary Litigation. 13 Cardozo Law Review 13: 657–704

    Google Scholar 

  • Sentient Sentient Informations Systems home page. [Online]. Available from: [Accessed 2004]

  • Soomro, T. R., Naqvi, M. R., and Zheng, K. (2001). GIS: A Weapon to Combat the Crime. In Proceedings of SCI 2001/ISAS 2001 (International Conference on Information Systems, Analysis and Synthesis), World Multiconference on Systemics, Cybernetics and Informatics, Information Systems Development: Part I

  • Speckt, D. F. (1990). Probabilistic Neural Networks. Neural Networks 3(1): 109–118

    Article  Google Scholar 

  • Stranieri, A. and Zeleznikow, J. (2000). Knowledge Discovery for Decision Support in Law. ICIS 2000: 635–639

    Google Scholar 

  • Tillers, P. and Schum, D. (1988). Charting New Territory in Judicial Proof: Beyond Wigmore. 9 Cardozo Law Review 907: 907–966

    Google Scholar 

  • Townsley, M. and Pease, K. (2002). How Efficiently can We Target Prolific Offenders?. International Journal of Police Science and Management 4(4): 323–331

    Article  Google Scholar 

  • Trajan Homepage of Trajan Software [Online]. Available from: [Accessed 2004, March 22]

  • Trickett, A., Osborn, D., Seymour, J., and Pease, K. (1992). What is Different About High Crime Areas?. British Journal of Criminology 32(1): 81–89

    Google Scholar 

  • Tversky, A. (1997). Features of Similarity. Psychology Review. 84: 327–352

    Article  Google Scholar 

  • Vargas, J. E. et al. (1998). Similarity-Based Reasoning about Diagnosis of Analogue Circuits. In Proceedings of the First International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. Tennessee, USA, 1988, 83–86. ACM Press: New York, USA

  • Wall, M. GAlib: A C++ Library of Genetic Algorithm Components, version 2.4. Matthew Wall – Mechanical Engineering Department, Massachusetts Institute of Technology., August 1996

  • Walton (2002) Legal Argumentation and Evidence. The Pennsylvania State University: University Park, Pennsylvania

    Google Scholar 

  • Weiss, S. M. and Indurkhya, N. (1998). Predictive Data Mining. Morgan Kaufmann Publishers: San Francisco, California, USA

    MATH  Google Scholar 

  • Wielemaker, J. (2003). An Overview of the SWI-Prolog Programming Environment. In Mesnard, F. and Serebenik, A. (eds.) Proceedings of the 13th International Workshop on Logic Programming Environments. pp. 1–16. Katholieke Universiteit Leuven: Heverlee, Belgium

    Google Scholar 

  • Wielemaker, J., Schreiber, G., and Wielinga, B. (2003). Prolog-based Infrastructure for RDF: Performance and Scalability. In Fensel, D., Sycara, K., and Mylopoulos, J. (eds.), The Semantic Web – Proceedings ISWC’03, Sanibel Island, Florida, 644–658. Springer Verlag

  • Wigmore, J. H. (1913). The Principles of Judicial Proof. Little Brown and Company: Boston, Massachussetts

    Google Scholar 

  • Williamson, D., McLafferty, S., McGuire, P., Ross, T., Mollenkopf, J., Goldsmith, V., and Quinn, S. (2001). Tools in the Spatial Analysis of Crime. In Hirschfield, A. and Bowers, K. (eds.) Mapping and Analysing Crime Data-Lessons from Research and Practice. pp. 187–203. Taylor and Francis: London, New York

    Google Scholar 

  • Wilson, D. C. (2001). Case-Base Maintenance: The Husbandry of Experience. Ph.D. thesis. Department of Computer Science, University of Indiana, USA

  • Wilson, I. D., Corcoran, J., and Ware, J. A. (2002). Predicting the Geo-temporal Variations of Crime and Disorder. In Proceedings of the Sixth Annual International Crime Mapping Research Conference: Bridging the Gap Between Research and Practice, Denver, Colorado

  • WinBugs. WinBUGS home page. [Online]. Available from: [Accessed 2004]

  • Witten, I. H. and Frank, E. (1999). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, ISBN 1-55860-552-5

  • Yokota, K. and Watanabe, S. (2002). Computer Based Retrieval of Suspects Using Similarity of Modus Operandi. International Journal of Police Science and Management 4(1): 5–15

    Google Scholar 

  • Zeleznikow, J. (2002). Designing Decision Support Systems for Crime Investigation. In Proceedings of the Fifth International Conference on Forensic Statistics (ICFS5), Isola di San Servolo, Venice, Italy, August 30 – September 2, 2002

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Oatley, G., Ewart, B. & Zeleznikow, J. Decision support systems for police: Lessons from the application of data mining techniques to “soft” forensic evidence. Artif Intell Law 14, 35–100 (2006).

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