Crime against Women (CAW) Analysis and Prediction in Tamilnadu Police Using Data Mining Techniques
S. Lavanyaa1, D. Akila2
1S. Lavanyaa, Ph.D Research Scholar, Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies VISTAS, Chennai (Tamil Nadu), India.
2D. Akila, Associate Professor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies VISTAS, Chennai (Tamil Nadu), India.
Manuscript received on 13 February 2019 | Revised Manuscript received on 09 April 2019 | Manuscript Published on 28 April 2019 | PP: 261-265 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10600275C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Crime examination and anticipation is a deliberate methodology for recognizing and breaking down examples and patterns in wrongdoing against ladies in Tamilnadu state. Our framework can anticipate metropolitan urban communities which have expansive volume of individuals living territories, workplaces, and corporate organizations intended forcrime event and can representation crime point regions. Through the getting higher entry to methodical, crimein sequence experts is capable offacilitate the rule implementation heads and extraordinary examination police groups to attach the further enquiries of understanding violations. To apply the origination of in sequence mining we can take out already unidentified, valuable data from unstructured information. In this paper, we are in push toward software engineering and criminal equity to enhance an information mining system that can help unravel Crimes against ladies quicker. As an alternate specializing in interference of crime prevalence like criminal background of crook and conjointly we tend to area unit concentrating primarily on the crime factors of each day.
Keywords: Crime Patterns, Classification and Prediction, Crimes Against Women (CAW), Rule Enlistment, Crime Analysis.
Scope of the Article: Data Mining