Abstract
While businesses have been extensively using data mining to pursue everlasting prosperity, we seldom consider this technique in public affairs. The government holds a large quantity of data that are records of official operations or private information of the people. These data can be used for increasing benefits of the people or enhancing the efficiency of governmental operations. In this paper we will apply this technique to the data of stolen automobiles to explore the unknown knowledge hidden in the data and provide this knowledge to transportation, insurance as well as law enforcement for decision supports. The data we use are abstracted from 378 thousand records of stolen automobiles in the past eleven years in Taiwan. After constructing a data warehouse, we apply the technique of classification, association rule, prediction, data generalization and summarization-based characterization to discover new knowledge. Our results include the understanding of automotive theft, possibility of finding stolen automobiles, intrigue in theft claims, etc. The knowledge we acquired is useful in decision support, showing the applicability of data mining in public affairs. The experience we gathered in this study would help the use of this technique in other public sectors. Along with the research results, we suggest the law enforcement to consider data mining as a new means to investigate criminal cases, to set up a team of criminal data analysis, to launch a new program to crack down automotive thefts, and to improve the quality of criminal data management.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Coleman, C., Moynihan, J.: Understanding Crime Data. Open University Press, Milton Keynes (1996)
Barclay, P., Buckley, J., Brantingham, P.J., Brantingham, P.L., Whinn-Yates, T.: Preventing Auto Theft in Suburban Vancouver Commuter Lots: Effects of a Bike Patrol. In: Clarke, R. (ed.) Crime Prevention Studies, vol. 6, pp. 133–162. Criminal Justice Press, New York (1996)
Clarke, R.V.: Theoretical Background to Crime Prevention through Environmental Design (CPTED) and Situational Prevention. In: Geason, S., Wilson, P. (eds.) Designing Out Crime: The Conference Papers, pp. 13–20. Australian Institute of Criminology (1989)
Sanders, W.B.: Juvenile Delinquency. Praeger, New York (1976)
Chilton, R.J.: Middle Class Delinquency and Specific Offence Analysis. In: Vaz, E.W. (ed.) Middle-Class Juvenile Delinquency, pp. 91–101. Harper & Row, New York (1967)
Schepses, E.: Boys Who Steal Cars. Federal Probation, pp. 56–62 (1961)
Clarke, R.V., Harris, P.M.: Auto Theft and Its Prevention. In: Tonry, M. (ed.) Crime and Justice: A Review of Research, vol. 16, University of Chicago Press, Chicago (1992)
Massey, J.L., Krohn, M.D., Bonati, L.M.: Property Crime and the Routine Activity of Individuals. Journal of Research in Crime and Delinquency 26, 378–400 (1989)
Hauck, R.V., Chen, H.: Coplink: a case of intelligent analysis and knowledge management. In: Proceeding of the 20th international conference on Information Systems, pp. 15–27 (1999)
Pliant, L.: High-technology Solutions. The Police Chief 5(38), 38–51 (1996)
Bowen, J.E.: An Expert System for Police Investigators of Economic Crimes. Expert Systems with Applications 7(2), 235–248 (1994)
Brahan, J.W., Lam, K.P., Chan, H., Leung, W.: AICAMS: Artificial Intelligence Crime Analysis and Management System. Knowledge-Based Systems 11, 355–361 (1998)
Atabakhsh, H., Schroeder, J., Chen, H., Chau, M., Xu, J., Zhang, J., Bi, H.: COPLINK knowledge management for law enforcement: Text analysis, visualization and collaboration. In: National Conference on Digital Government, Los Angeles, CA, pp. 21–23 (2001)
Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. IEEE Press & John Wiley (2002)
Chen, H., Chung, W., Xu, J., Wang, G., Chau, M.: Crime Data Mining: A General Framework and Some Examples, pp. 50–56. ’ IEEE Computer Society, Los Alamitos (2004)
Han, J., Kamber, M.: Data Ming: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
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
Chen, P.S., Chang, K.C., Hsing, TP., Chou, S. (2006). Mining Criminal Databases to Finding Investigation Clues—By Example of Stolen Automobiles Database. In: Chen, H., Wang, FY., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds) Intelligence and Security Informatics. WISI 2006. Lecture Notes in Computer Science, vol 3917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11734628_12
Download citation
DOI: https://doi.org/10.1007/11734628_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33361-6
Online ISBN: 978-3-540-33362-3
eBook Packages: Computer ScienceComputer Science (R0)