Efficient frequent pattern knowledge for crime situation recognition in developing countries

Proceedings of the 4th Annual Symposium on Computing for Development, ACM DEV 2013

Volumn , Issue , 2013, Pages

  Isafiade, Omowunmi ^a   Bagula, Antoine ^a  

^a UNIVERSITY OF CAPE TOWN   (South Africa)

Author keywords

Crime Situation Recognition and Intelligent Platforms; Data Mining; Public safety

Indexed keywords

DATA MINING; DEVELOPING COUNTRIES; TOOLS;

CRIME DATABASE; CRIME PREVENTION; FP-GROWTH ALGORITHM; LAW-ENFORCEMENT AGENCIES; MINING PROCESS; PUBLIC SAFETY; RESEARCH FOCUS; SITUATION RECOGNITION;

CRIME;

EID: 84893381043     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2537052.2537073     Document Type: Conference Paper

References (5)

1. SAPS together squeezing crime to zero, September 2011. http://www.saps.gov.za Annual Report 2010/2011 South African Police Service.
2. Crime statistics overview RSA 2011/ 2012, May 2012. http://www.saps.gov. za/statistics/reports/crimestats/fhospi.
3. Victims of crime survey, September 2012. P0341 Statistics South Africa.
4. W. Yinshang, G. Yi, D. Dingwen, and L. Dewen. Public places safety management evaluation of railway stations. In Proceedings of the International Symposium on Safety Science and Technology, Sciverse Science Direct, pages 240-247, March 2012.
5. Ziauddin, S. Kammal, K. Khan, and M. Khan. Research on association rule mining. Journal of Advances in Computational Mathematics and its Applications (ACMA), 2(1):226-236, July 2012.