An Approach to Mining Crime Patterns

International Journal of Data Warehousing and Mining (IJDWM)

Volumn 2, Issue 1, 2006, Pages 50-80

  Bagui, Sikha ^a  

^a UNIVERSITY OF WEST FLORIDA   (United States)

Author keywords

association rules; attribute analysis; clustering; data mining; data preprocessing; decision tree; SOM

Indexed keywords

EID: 85001764901     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2006010103     Document Type: Article

References (26)

1. Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD Conference, Washington, DC (pp. 207–216).
2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, Santiago de Chile (pp. 478–499).
3. Alahakoon, D., Halgamuge, S. K., & Srinivasan, B. (2000). Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks, Special Issue on Knowledge Discovery and Data Mining, 11(3), 601–614.
4. Bagui, S., & Earp, R. (2004). Learning SQL: A step-by-step guide using access. Boston: Addision Wesley.
5. Chen, S. Y., & Liu, X. (2005). Data mining from 1994–2004: An application-oriented review. International Journal of Business Intelligence and Data Mining, 1(1), 4–21.
6. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). (1996). Advances in knowledge discovery and data mining. Menlo Park, CA: AAAI/MIT Press.
7. Gupta, A., Harinarayan, V., & Quass, D. (1995). Aggregate-query processing in data warehousing environment.In Proceedings of the 21st International Conference on Very Large Data Bases, Zurich, Switzerland (pp. 358–369).
8. Han, J., Cai, Y., & Cercone, N. (1993). Data-driven discovery of quantitative rules in relational databases. IEEE Trans. Knowledge and Data Engineering, 5, 29–40.
9. Han, J., & Kamber, M. (2001). Data mining: Concepts and technique. Boston: Morgan Kaufmann.
10. Harinarayan, V., Ullman, J. D., & Rajaraman, A. (1996). Implementing data cubes efficiently. In Proceedings of the 1996 ACM SIGMOD International Conference on Management Data (pp. 205–216), Montreal, Canada.
11. Hu, X., Song, Il-Y., Han, H., Yoo, I., Prestrud, A. A., Brennan, M. F., et al. (2005). Temporal rule induction for clinical outcome analysis. International Journal of Business Intelligence and Data Mining, 1(1), 122–136.
12. Jin, H.-D., Shum, W.-H., Leung, K.-S., & Wong, M.-L. (2004). Expanding self-organizing map for data visualization and cluster analysis. Information Sciences, 163(1–3), 157–173.
13. Kaggalage, R., Halgamuge, S., & Hsu, A. L. C. (2004). Unsupervised class discovery and feature selection using an improved hierarchical dynamic self-organizing map. Neural Information Processing - Letters and Reviews, 3(1), 1–10.
14. Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
15. Neural network based clustering using self-organizing map (SOM) in Excel (n.d.). Retrieved July 2005, from http:/ /www.geocities.com/adotsaha/NN/ SOMinExcel.html
16. Par, J. S., Chen, M. S., & Yu, P. S. (1995). An effective hash based algorithm for mining association rules. In Proceedings of the ACM SIGMOD, San Jose, CA (pp. 175–186).
17. Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rule. In G. Piatetsky-Shapiro, & W. J. Frawley (Eds.), Knowledge discovery in databases (pp. 229–238). Boston: AAAI/MIT Press.
18. Roiger, R., & Geatz, M. (2003). Data mining: A tutorial-based primer. Boston: Addison Wesley.
19. Srikant, R., & Agrawal, R. (1997). Mining generalized association rules. Future Generation Computer Systems, 13, 2–3.
20. State Politics and Policy Quarterly Data Resource. (n.d.). Retrieved December 2004, from http://www.unl.edu/SPPQ/ datasets.html
21. Tan, P.-N., SteinBach, M., & Kumar, V. (2006). Introduction to data mining. Boston: Addison Wesley.
22. Tjioe, H. E., & Taniar, D. (2005). Mining association rules in data warehouses. International Journal of Data Warehousing and Mining, 1(3), 28–62.
23. Weka 3: Data mining software in Java (n.d.). Retrieved December 2004, from http://www.cs.waikato.ac.nz/ml/ weka
24. Widom, J. (1995). Research problems in data warehousing. In Proceedings of the Fourth International Conference on Information and Knowledge Management (pp. 25–30), Baltimore, Maryland.
25. Witten, I. H., & Frank, E. (2000). Data mining: Practical machine learning tools and techniques with Java implementations. Boston: Morgan Kaufmann.
26. Yan, W. P., & Larson, P. (1995). Eager aggregation and lazy aggregation. In Proceedings of the 21st International Conference on Very Large Data Bases (pp. 345–357), Zurich, Switzerland.