Abstract
The information explosion has led to problems and possibilities in many areas of society, including that of law enforcement. In comparing individual criminal investigations on similarity, we seize one of the opportunities of the information surplus to determine what crimes may or may not have been committed by the same group of individuals.
For this purpose we introduce a new distance measure that is specifically suited to the comparison between investigations that differ largely in terms of available intelligence. It employs an adaptation of the probability density function of the normal distribution to constitute this distance between all possible couples of investigations.
We embed this distance measure in a four-step paradigm that extracts entities from a collection of documents and use it to transform a high dimensional vector table into input for a police operable tool. The eventual report is a two-dimensional representation of the distances between the various investigations and will assist the police force on the job to get a clearer picture of the current situation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adderley, R., Musgrove, P.B.: Data mining case study: Modeling the behavior of offenders who commit serious sexual assaults. In: KDD 2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, pp. 215–220 (2001)
Chau, M., Atabakhsh, H., Zeng, D., Chen, H.: Building an infrastructure for law enforcement information sharing and collaboration: Design issues and challenges. In: Proceedings of The National Conference on Digital Government Research (2001)
Chau, M., Xu, J., Chen, H.: Extracting meaningful entities from police narrative reports. In: Proceedings of The National Conference on Digital Government Research (2002)
Chen, H., Atabakhsh, H., Petersen, T., Schroeder, J., Buetow, T., Chaboya, L., O’Toole, C., Chau, M., Cushna, T., Casey, D., Huang, Z.: COPLINK: Visualization for crime analysis. In: Proceedings of the The National Conference on Digital Government Research (2003)
Davison, M.L.: Multidimensional Scaling. John Wiley, New York (1983)
Goldberg, H.G., Wong, R.W.H.: Restructuring transactional data for link analysis in the FinCEN AI system. In: Papers from the AAAI Fall Symposium (1998)
Kosters, W.A., Marchiori, E., Oerlemans, A.A.J.: Mining clusters with association rules. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds.) IDA 1999. LNCS, vol. 1642, pp. 39–50. Springer, Heidelberg (1999)
Kosters, W.A., van Wezel, M.C.: Competitive neural networks for customer choice models. In: E-Commerce and Intelligent Methods, Studies in Fuzziness and Soft Computing, pp. 41–60. Physica-Verlag, Springer, Heidelberg (2002)
Kumar, N., de Beer, J., Vanthienen, J., Moens, M.-F.: Evaluation of intelligent exploitation tools for non-structured police information. In: Proceedings of the ICAIL 2005 Workshop on Data Mining, Information Extraction and Evidentiary Reasoning for Law Enforcement and Counter-terrorism (2005)
Oatley, G.C., Zeleznikow, J., Ewart, B.W.: Matching and predicting crimes. In: Proceedings of AI 2004, the Twenty-fourth SGAI International Conference on Knowledge Based Systems and Applications of Artificial Intelligence (2004)
Skillicorn, D.B.: Clusters within clusters: SVD and counterterrorism. In: Proceedings of the Workshop on Data Mining for Counter Terrorism and Security (2003)
SPSSÂ LexiQuest website, http://www.spss.com/spssbi/lexiquest/
Xiang, Y., Chau, M., Atabakhsh, H., Chen, H.: Visualizing criminal relationships: Comparison of a hyperbolic tree and a hierarchical list. Decision Support Systems 41(1), 69–83 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cocx, T.K., Kosters, W.A. (2006). A Distance Measure for Determining Similarity Between Criminal Investigations. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_40
Download citation
DOI: https://doi.org/10.1007/11790853_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36036-0
Online ISBN: 978-3-540-36037-7
eBook Packages: Computer ScienceComputer Science (R0)