Learning to detect patterns of crime

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8190 LNAI, Issue PART 3, 2013, Pages 515-530

  Wang, Tong ^a   Rudin, Cynthia ^a   Wagner, Daniel ^b   Sevieri, Rich ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b Walter J Sullivan Water Purification Facility   (United States)

Author keywords

crime data mining; Pattern detection; predictive policing

Indexed keywords

CAMBRIDGE; CRIME DATA MINING; MAJOR CITIES; PATTERN DATUM; PATTERN DETECTION; PATTERN DETECTION ALGORITHMS; PREDICTIVE POLICING;

DATABASE SYSTEMS; LEARNING SYSTEMS; TOOLS;

CRIME;

EID: 84886580564     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40994-3_33     Document Type: Conference Paper

References (25)

1. Berk, R., Sherman, L., Barnes, G., Kurtz, E., Ahlman, L.: Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning. Journal of the Royal Statistical Society: Series A (Statistics in Society) 172(1), 191-211 (2009)
2. Pearsall, B.: Predictive policing: The future of law enforcement? National Institute of Justice Journal 266, 16-19 (2010)
3. Gwinn, S.L., Bruce, C., Cooper, J.P., Hick, S.: Exploring crime analysis. Readings on essential skills, 2nd edn. BookSurge, LLC (2008)
4. Ratcliffe, J.H., Rengert, G.F.: Near-repeat patterns in Philadelphia shootings. Security Journal 21(1), 58-76 (2008)
5. Dahbur, K., Muscarello, T.: Classification system for serial criminal patterns. Artificial Intelligence and Law 11(4), 251-269 (2003)
6. Nath, S.V.: Crime pattern detection using data mining. In: Proceedings of Web Intelligence and Intelligent Agent Technology Workshops, pp. 41-44 (2006)
7. Brown, D.E., Hagen, S.: Data association methods with applications to law enforcement. Decision Support Systems 34(4), 369-378 (2003)
8. Lin, S., Brown, D.E.: An outlier-based data association method for linking criminal incidents. In: Proceedings of the Third SIAM International Conference on Data Mining. (2003)
9. Ng, V., Chan, S., Lau, D., Ying, C.M.: Incremental mining for temporal association rules for crime pattern discoveries. In: Proceedings of the 18th Australasian Database Conference, vol. 63, pp. 123-132 (2007)
10. Buczak, A.L., Gifford, C.M.: Fuzzy association rule mining for community crime pattern discovery. In: ACM SIGKDD Workshop on Intelligence and Security Informatics (2010)
11. Wang, G., Chen, H., Atabakhsh, H.: Automatically detecting deceptive criminal identities. Communications of the ACM 47(3), 70-76 (2004)
12. Chen, H., Chung, W., Xu, J., Wang, G., Qin, Y., Chau, M.: Crime data mining: a general framework and some examples. Computer 37(4), 50-56 (2004)
13. Hauck, R.V., Atabakhsb, H., Ongvasith, P., Gupta, H., Chen, H.: Using COPLINK to analyze criminal-justice data. Computer 35(3), 30-37 (2002)
14. Short, M.B., D'Orsogna, M.R., Pasour, V.B., Tita, G.E., Brantingham, P.J., Bertozzi, A.L., Chayes, L.B.: A statistical model of criminal behavior. Mathematical Models and Methods in Applied Sciences 18, 1249-1267 (2008)
15. Mohler, G.O., Short, M.B., Brantingham, P.J., Schoenberg, F.P., Tita, G.E.: Self-exciting point process modeling of crime. Journal of the American Statistical Association 106(493) (2011)
16. Short, M.B., D'Orsogna, M., Brantingham, P., Tita, G.: Measuring and modeling repeat and near-repeat burglary effects. Journal of Quantitative Criminology 25(3), 325-339 (2009)
17. Eck, J., Chainey, S., Cameron, J., Wilson, R.: Mapping crime: Understanding hotspots. Technical report, National Institute of Justice, NIJ Special Report (August 2005)
18. Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: International Conference on Machine Learning, pp. 19-26 (2002)
19. Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: Int'l Conf. onMachine Learning, pp. 577-584 (2001)
20. Ghahramani, Z., Heller, K.: Bayesian sets. In: Proceedings of Neural Information Processing Systems (2005)
21. Letham, B., Rudin, C., Heller, K.: Growing a list. Data Mining and Knowledge Discovery (to appear, 2013)
22. Boriah, S., Chandola, V., Kumar, V.: Similarity measures for categorical data: A comparative evaluation. In: Proceedings of the Eighth SIAM International Conference on Data Mining, pp. 243-254 (2008)
23. Criminal Justice Policy Research Institute: Residential burglary in Portland, Oregon. Hatfield School of Government, Criminal Justice Policy Research Institute, http://www.pdx.edu/cjpri/time-of-dayday-of-week-0
24. Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The elements of statistical learning: data mining, inference and prediction. Springer (2005)
25. National Law Enforcement and Corrections Technology Center: 'Calculate' repeat crime. TechBeat (Fall 2008)