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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.
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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

    	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 = "",
    	doi = "10.5120/ijais2020451889",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


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 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.


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