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Comparative Evaluation of Crime Incidence using Enhanced Density based Spatial (Dbscan) Clustering

International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 8
Year of Publication: 2015
V. Vinodhini
M. Hemalatha

V.vinodhini and M.hemalatha. Article: Comparative Evaluation of Crime Incidence using Enhanced Density based Spatial (Dbscan) Clustering. International Journal of Computer Applications 122(8):16-19, July 2015. Full text available. BibTeX

	author = {V.vinodhini and M.hemalatha},
	title = {Article: Comparative Evaluation of Crime Incidence using Enhanced Density based Spatial (Dbscan) Clustering},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {8},
	pages = {16-19},
	month = {July},
	note = {Full text available}


Criminology is the cream of crimes, crime fatalities, theories of ill-legal and abnormal behavior, social exploration, anti-crime polices, the political terrain of social control. So criminology involves the factual deeds in the streets, police stations, and courts, behind prison bars, board rooms and battlefields. Its practitioners are likely to slot in the orderly appraisal of the effectiveness of criminal justice policies and proposals, as well as the discovery of the source-cultural, economic and global roots of crime, rates of crime and meaning of crime, or the diverse ways of measuring criminal activity and its impact. Criminologists typically accumulate and scrutinize data sets that may be quantified, for example statistical studies on the rise and fall of crime rates, and/or qualitative, for example ethnographic studies on street subcultures and drug use. The research work concentrates in bringing of qualitative and quantitative study of crime rates and their behaviors. Here in this research work Density based spatial clustering is compared and analyzed with an enhanced DBSCAN algorithm, the results are also grouped in order to provide the efficiencies of crime rates. The outlook research work can be resolved by enhancing hybrid models in order to have condensed outlier detection.


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