Crime analysis in India using data mining techniques

  • Authors

    • Deepika K K
    • Smitha Vinod
    2018-03-11
    https://doi.org/10.14419/ijet.v7i2.6.10779
  • Clustering, Classification, Visualization, K-means, Random Forest, Neural Networks.

  • Abstract

    An approach for crime detection in India using Data mining techniques is proposed in this paper. The approach consists of the following steps - Data pre-processing, clustering, classification and visualization. Data mining techniques are often applied to Criminology as it provides good results. Criminology is a field which studies about various crime characteristics. Analyzing crime data means exploring crime data. Crime is identified using k-means clustering and the clusters are formed based on the similarity of the crime attributes. The Random Forest algorithm and Neural networks are applied on the data for classification. Visualization is achieved using the Google marker clustering and the crime spots are marked on the India map. The accuracy is verified using WEKA tool. This approach will benefit the Crime department of India in analyzing crime with better prediction. The paper focuses on the crime analysis of various Indian states and union territories during 2001 to 2012.

     

     

  • References

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  • How to Cite

    K K, D., & Vinod, S. (2018). Crime analysis in India using data mining techniques. International Journal of Engineering & Technology, 7(2.6), 253-258. https://doi.org/10.14419/ijet.v7i2.6.10779

    Received date: 2018-03-29

    Accepted date: 2018-03-29

    Published date: 2018-03-11