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Crime detection and criminal identification in India using data mining techniques

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Abstract

In the current paper, we propose an approach for the design and implementation of crime detection and criminal identification for Indian cities using data mining techniques. Our approach is divided into six modules, namely—data extraction (DE), data preprocessing (DP), clustering, Google map representation, classification and WEKA® implementation. First module, DE extracts the unstructured crime dataset from various crime Web sources, during the period of 2000–2012. Second module, DP cleans, integrates and reduces the extracted crime data into structured 5,038 crime instances. We represent these instances using 35 predefined crime attributes. Safeguard measures are taken for the crime database accessibility. Rest four modules are useful for crime detection, criminal identification and prediction, and crime verification, respectively. Crime detection is analyzed using k-means clustering, which iteratively generates two crime clusters that are based on similar crime attributes. Google map improves visualization to k-means. Criminal identification and prediction is analyzed using KNN classification. Crime verification of our results is done using WEKA®. WEKA® verifies an accuracy of 93.62 and 93.99 % in the formation of two crime clusters using selected crime attributes. Our approach contributes in the betterment of the society by helping the investigating agencies in crime detection and criminals’ identification, and thus reducing the crime rates.

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Notes

  1. Jessica Lal murder case, http://www.ndtv.com/topic/jessica-lal Accessed on April 28, 2013.

  2. Nithari case, http://wcd.nic.in/nitharireport.pdf Accessed on April 30, 2013.

  3. Mumbai blast, http://www.ipcs.org/pdf_file/issue/SR71-Final.pdf Accessed on April 28, 2013.

  4. Delhi rape case, http://www.dailymail.co.uk/news/article-2269725/Indian-teenager-accused-Delhi-gang-rape-faces-maximum-year-jail-term.html Accessed on April 28, 2013.

  5. National Crime Records Bureau, http://ncrb.gov.in Accessed on March 10, 2013.

  6. Committee to Protect Journalists, http://www.cpj.org Accessed on March 20, 2013.

  7. Crime alert, http://www.crimealert.org Accessed on March 25, 2013.

  8. NSW bureau of crime statistics and research, http://www.bocsar.nsw.gov.au Accessed on March 22, 2013.

  9. Jin F, Wang W, Xiao Y, Pan Z Proposal of Crime Data Mining Project. https://filebox.vt.edu/users/xykid/dataAnalysisProject/Checkpoint-II_Jin_Xiao_Pan_Wang.pdf. Accessed on May 30, 2013.

  10. Netbeans, http://netbeans.org Accessed on April 2, 2013.

  11. WEKA manual, http://www.inf.ufpr.br/lesoliveira/aprendizado/wekamanual.pdf Accessed on March 15, 2013.

  12. Crime wave in India, http://www.merinews.com/article/the-crime-wave-in-india/132433.shtml Accessed on April 18, 2013.

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Authors and Affiliations

  1. Department of CSE, Indira Gandhi Delhi Technical University for Women, New Delhi, India

    Devendra Kumar Tayal

  2. Department of CSE/IT, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Arti Jain

  3. Department of IT, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Surbhi Arora, Surbhi Agarwal, Tushar Gupta & Nikhil Tyagi

Authors
  1. Devendra Kumar Tayal
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  2. Arti Jain
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  3. Surbhi Arora
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  4. Surbhi Agarwal
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  5. Tushar Gupta
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  6. Nikhil Tyagi
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Correspondence to Arti Jain.

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Tayal, D.K., Jain, A., Arora, S. et al. Crime detection and criminal identification in India using data mining techniques. AI & Soc 30, 117–127 (2015). https://doi.org/10.1007/s00146-014-0539-6

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