Descriptive Analytics on Crime in India using Clustering Techniques
J. Vimala Devi1, Kavitha K S2
1J Vimala Devi*, Department of Computer Science and Engineering, Visveswaraya Technological University, Bangalore, India.
2Dr Kavitha K S, Department of Computer Science and Engineering, Visveswaraya Technological University, Bangalore, India.
Manuscript received on December 163, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 2261-2265 | Volume-9 Issue-3, January 2020. | Retrieval Number: L29241081219/2020©BEIESP| DOI: 10.35940/ijitee.L2924.019320
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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: Understanding the perspectives of crime happenings by exploiting crime data helps early detection and prevention of crimes. Ruling Government and Policing systems are aware the importance of realising the changing aspects of crime. As technology is advanced, there are many ways to comprehend the trends and the patterns of crime activities. The paper presents a hybrid model using AGNES and K-means clustering algorithms to focus on different views and representation of crime in India and aims to recognize the crime types that cluster the regions of India. Accuracy of model is measured using log loss and the patterns and trends in crime are presented.
Keywords: Unsupervised learning, Clustering techniques, Machine learning, Crime data analytics
Scope of the Article: Machine learning