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November Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the November 2021 Edition of the journal. The last date of research paper submission is October 15, 2021.

Crime Analysis Tool using Kernelized Fuzzy C-Means (KFCM) Algorithm

Adeyiga J.A., Achas M.J., Adewumi O.A. in Algorithms

International Journal of Applied Information Systems
Year of Publication:2020
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Adeyiga J.A., Achas M.J., Adewumi O.A.
10.5120/ijais2020451889
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  1. Adeyiga J.A., Achas M.J. and Adewumi O.A.. Crime Analysis Tool using Kernelized Fuzzy C-Means (KFCM) Algorithm. International Journal of Applied Information Systems 12(34):5-9, November 2020. URL, DOI BibTeX

    @article{10.5120/ijais2020451889,
    	author = "Adeyiga J.A. and Achas M.J. and Adewumi O.A.",
    	title = "Crime Analysis Tool using Kernelized Fuzzy C-Means (KFCM) Algorithm",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "November 2020",
    	volume = 12,
    	number = 34,
    	month = "November",
    	year = 2020,
    	issn = "2249-0868",
    	pages = "5-9",
    	url = "http://www.ijais.org/archives/volume12/number34/1103-2020451889",
    	doi = "10.5120/ijais2020451889",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

Several criminal analysis tools have been developed to assist the Law enforcement agency LEA in solving crimes but the techniques employed in most of the systems lack the ability to analysis criminal based on their behavioral characteristics. Hence, this research therefore developed a criminal analysis tool using the KFCM algorithm and compared the result with the FCM algorithm.

The data used was downloaded online and it is available at https://portal.chicagopolice.org/portal/page/portal/ClearPath/News/Crime%20 statistics from the city of Chicago Police Department with over one million records.

The paper reviewed the Fuzzy C-Means (FCM) clustering algorithm and the Kernelized Fuzzy C-Means algorithm and then implemented and compared the results of both algorithms using confusion matrix as the metric of evaluation.

The result analysis shows that the KFCM and the FCM algorithms both performed at par to each other but the KFCM had a better accuracy over the FCM algorithm with a higher execution time.

The FCM algorithm is therefore recommended to be modified along with the KFCM to give a more robust cluster with higher performance.

Reference

  1. Askeriya I, H. Jahankhani, S. Wee Lee and Ameer Al- Nemrat. (2010). Education, Training and Awareness (ETA) Four Dimensional Cybercrime prevention model; 24th European Conference on Information Systems, (12-15) Istanbul, Turkey.
  2. Dellaert Frank (2002). The Expectation Maximization Algorithm; Technical report Georgia Institute of Technology, 2002.
  3. Esh Narayan, Yogesh Birla and Gaurav Tax (2012). Enhancement of Fuzzy C-means Clustering using Expectation Maximization Algorithm; International Journal of Computer Applications, Vol 43 no 13 April 2012.
  4. Krishnamurthy Revatthy (2012). Survey of Data Mining Techniques on Crime Data; International Journal of Data Mining Techniques and Applications, Vol. 01: 48 –55.
  5. Klawonn Hoppner F, F Fruse R and Runkler (2003). Fuzzy Cluster Analysis; J. Wiley and Sons, Chichester, England 2003.
  6. Khare Pallavi, Anagha Gaikwad, and Pooja Kumari, (2015). Fuzzy C- Means Clustering with Kernel Metric and Local Information for Image Segmentation; International Journal of Computer Applications (0975 –8887) National Conference on Emerging Trends in Advanced Communication Technologies (NCETACT- 2015).
  7. Prabhakar Sandhya H, Sandeep Kumar, (2017). A Survey on Fuzzy C-Means Clustering Techniques; International Journal of Engineering Development and Research 2017 IJEDR | Vol 5, Issue 4 | ISSN: 2321-9939.
  8. Raphael O. O and Francis O.E (2011). Combating Crime and Terrorism Using Data Mining Techniques; 10th International Conference on Information Technology for People Centred Development Nigeria Computer Science Conference Proceedings, Vol 22: 80 – 89, 2011.
  9. Sheeba M. S. and A. Sathya (2015) “Hybrid approach of Kernelized Fuzzy C-Means and Support Vector Machine for Breast Medical Image Segmentation” Journal of Chemical and Pharmaceutical Research, 2015, 7(2):281-291.
  10. Shyam Varan Nath, (2006). Crime pattern detection using Data Mining; Proceeding of the 2006, International Conference on Web Intelligence and Intelligence Agent, 41-44.
  11. City of Chicago police Department (2001) via city of Chicago.org/public-safety/crimes-2001-to present. available at https://portal.chicagopolice.org/portal/page/portal/ClearPath/News/Crime%20Statistics

Keywords

Kernelized fuzzy c-means, law enforcement agency, clustering, clustering algorithm, Analysis, Crime data, Euclidean distance