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  • 标题:Feature Specific Criminal Mapping using Data Mining Techniques and Generalized Gaussian Mixture Model
  • 本地全文:下载
  • 作者:Uttam Mande ; Y.Srinivas ; J.V.R.Murthy
  • 期刊名称:International Journal of Computer Science and Communication Networks
  • 电子版ISSN:2249-5789
  • 出版年度:2012
  • 卷号:2
  • 期号:3
  • 页码:375-379
  • 出版社:Technopark Publications
  • 摘要:Lot of research is projected to map the criminal with that of crime and it is observed that there is still a huge increase in the crime rate due to the gap between the optimal usage of technologies and investigation. This has given scope for the development of new methodologies in the area of crime investigation using the techniques based on data mining, image processing, forensic, and social mining. In this paper, presents a model using new methodology for mapping the criminal with the crime. This model clusters the criminal data basing on the type crime. When a crime occurs, based on the eye witness specified features, the criminal is mapped. Here we propose a novel methodology that uses Generalized Gaussian Mixture Model to map the features specified by the eyewitness with that of the features of the criminal who have committed the same type of the crime, if the criminal is not mapped, the suspect table is checked and the reports are generated.
  • 关键词:Data mining; Generalized Gaussian Mixture model; Crime; criminal mapping; criminal identification; forensics
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