CFP last date
20 January 2025
Reseach Article

Mining Popular Crime Patterns in Spatial Databases

by B.V.S. Varma, V. Valli Kumari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 131 - Number 18
Year of Publication: 2015
Authors: B.V.S. Varma, V. Valli Kumari
10.5120/ijca2015907686

B.V.S. Varma, V. Valli Kumari . Mining Popular Crime Patterns in Spatial Databases. International Journal of Computer Applications. 131, 18 ( December 2015), 43-48. DOI=10.5120/ijca2015907686

@article{ 10.5120/ijca2015907686,
author = { B.V.S. Varma, V. Valli Kumari },
title = { Mining Popular Crime Patterns in Spatial Databases },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 18 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number18/23552-2015907686/ },
doi = { 10.5120/ijca2015907686 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:27:45.432123+05:30
%A B.V.S. Varma
%A V. Valli Kumari
%T Mining Popular Crime Patterns in Spatial Databases
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 18
%P 43-48
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Crime is one of the major threats to the society and is increasing rapidly in these days, so in order to control the crime rates many techniques and methods were brought into practise for the safety of the public. So such important task is given to the police by the data analysts. So, in this paper the proposing system mines popular crime patterns from spatial databases. Since the main part of the investigation will starts with the crime incident and the place of the crime. Therefore crime incident and place play a major role in mining process. This system helps in finding popular crime patterns speedy and results in better outputs.

References
  1. H. Chen, W. Chung, J. Xu, G. Wang, Y. Qin, M. Chau, Crime data mining: A general framework and some examples, IEEE Computer Journal 37 (4) (2004) 50–56.
  2. A. Mohammad, J. Mohsen, E. Martin, G. Uwe, F. Richard, Crimewalker: A recommendation model for suspect investigation, in: Proc. fifth ACM conference on Recommender systems, ACM, 2011, pp. 1–8.
  3. N. G. Khan, V. Bhaga, Effective data mining approach for crime-terrorpattern detection using clustering algorithm technique, Engineering Research and Technology International Journal Vol 2 (4) (2013), pp. 2043–2048.
  4. P. Phillips, I. Lee, Mining co-distribution patterns for large crime datasets, Expert Systems with Applications International Journal 39 (14) (2012) 11556–11563.
  5. O. Isafiade, A. Bagula, Citisafe: Adaptive spatial pattern knowledge using fp-growth algorithm for crime situation recognition, in: Proc. IEEE International Conference on Ubiquitous Intelligence and Computing, IEEE, 2013, pp. 551–556.
  6. D. Wang, W. Ding, H. Lo, T. Stepinski, J. Salazar, M. Morabito, Crime hotspot mapping using the crime related factors- a spatial data mining approach, Applied Intelligence Journal 39 (4) (2013) 772–781.
  7. Brown, D.E. The regional crime analysis program (RECAP): A frame work for mining data to catch criminals," in Proceedings of the IEEE International J.S. Yeh, S.C. Lin, “A New Data Structure for Asynchronous Periodic Pattern Mining”, Proc. 3rd Int’l Conf. Ubiquitous Information Management and Communication, pp. 426-431, 2009.
  8. Animesh Tripathy, Subhalaxmi Das, Prashanta KumarPatra “An Intelligent approach for mining frequent patterns in spatial data base systems using SQL” IEEE 2012.
  9. J. Han, W. Gong, Y. Yin, “Mining Segment-Wise Periodic Patterns in Time Related Databases,” Proc. ACM Int’l Conf. Knowledge Discovery and Data Mining, pp. 214-218, 1998.
  10. J. Yang, W. Wang, and P. S. Yu, “Mining Asynchronous Periodic Patterns in Time Series Data”, IEEE Trans. on Knowledge and Data Engineering, Vol. 15, Issue 3, pp. 613-628, 2003
  11. K. Y. Huang, C.H. Chang, “SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases”, IEEE Trans. On Knowledge and Data Engineering, Vol. 17, Issue 6, pp. 774-785, Jun, 2005.
  12. De Bruin, J.S. , Cocx, T.K. , Kosters, W.A. , Laros, J. And Kok, J.N. Data mining approaches to criminal career analysis,” in Proceedings of the Sixth International Conference on Data Mining (ICDM‟06), Pp. 171-177 (2006).
  13. Abraham, T. and de Vel, O. Investigative profiling with computer forensic log data and association rules," in Proceedings of the IEEE International Conference on Data Mining (ICDM'02), Pp. 11 – 18 (2006).
  14. BVS Varma, V. Valli Kumari.: Mining Popular Crime Patterns from Crime Datasets, Vol 4, Issue 5, October 2015, pp. 741 – 748.
Index Terms

Computer Science
Information Sciences

Keywords

Frequent patterns Popular patterns Crime patterns Crime Database Spatial Database.