Crime analysis in India using data mining techniques

 
 
 
  • Abstract
  • Keywords
  • References
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  • 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.

     

     


  • Keywords


    Clustering, Classification, Visualization, K-means, Random Forest, Neural Networks.

  • References


      [1] G. Jiji-S. Anantharadha, “Automatic Tracking of Criminals using Data Mining Techniques”, Journal of The Institution of Engineers (India): Series B, 2012, https://doi.org/10.1007/s40031-013-0036-1

      [2] Devendra Tayal, Arti Jain, Surbhi Arora, Surbhi Agarwal, Tushar Gupta, Nikhil Tyagi, “Crime detection and Criminal identification in India using data mining technique”, Ai & Society, 2014, https://doi.org/10.1007/s00146-014-0539-6

      [3] Shiju Sathyadevan, Devan S, Surya S, “Crime analysis and prediction using data mining”, First International Conference on Networks & Soft Computing (ICNSC2014), 2014, DOI: 10.1109/CNSC.2014.6906719

      [4] Shyam Nath, “Crime Pattern Detection Using Data Mining”, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, 2006, DOI: 10.1109/WI-IATW.2006.55

      [5] Somayeh Shojaee, Aida Mustafa, Fatimah Sidi, Marzanah Jabar, “A Study on Classification Learning Algorithms to Predict Crime Status”, International Journal of Digital Content Technology and its Applications(JDCTA), Volume 7, Number 9, 1-3, 2013, DOI: 10.4156/jdcta.vol7.issue9.43.

      [6] Dawei Wang, Wei Ding, Henry Lo, Tomasz Stepinski, Josue Salazar, Melissa Morabito, “Crime hotspot mapping using the crime related factors—a spatial data mining approach”, Applied Intelligence, 2012, https://doi.org/10.1007/s10489-012-0400-x

      [7] Chung-Hsien Yu, Max Ward, Melissa Morabito, Wei Ding, “Crime Forecasting Using Data Mining Techniques”, 2011 IEEE 11th International Conference on Data Mining Workshops, 2011, DOI: 10.1109/ICDMW.2011.56

      [8] Arunima Kumar, Raju Gopal, “Data mining based crime investigation systems: Taxonomy and relevance”, 2015 Global Conference on Communication Technologies (GCCT) – 2015, DOI: 10.1109/GCCT.2015.7342782

      [9] Prajakta Yerpude and Vaishnavi Gudur, “Predictive Modelling of Crime Dataset Using Data Mining”, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.7, No.4, July 2017, DOI: 10.5121/ijdkp.2017.7404

      [10] Mohammad Keyvanpour, Mostafa Javideh, Mohammad Ebrahimi, “Detecting and investigating crime by means of data mining: a general crime matching framework”, Procedia Computer Science,2011, https://doi.org/10.1016/j.procs.2010.12.143

      [11] Ubon Thongsatapornwatana, “A survey of data mining techniques for analyzing crime patterns”, 2016 Second Asian Conference on Defence Technology (ACDT), 2016, DOI:

      [12] 10.1109/ACDT.2016.7437655


 

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Article ID: 10779
 
DOI: 10.14419/ijet.v7i2.6.10779




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