International Journal of Data Science and Analysis
Volume 6, Issue 1, February 2020, Pages: 20-31
Received: Jan. 8, 2020;
Accepted: Jan. 31, 2020;
Published: Feb. 14, 2020
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Stephen Mangara Wainana, Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
Joseph Njuguna Karomo, Department of Pure and Applied Sciences, Kirinyaga University, Nairobi, Kenya
Rachael Kyalo, Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
Noah Mutai, Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
Crimes have been the most dangerous threat to peace, development, human right, social, political and economic stability in Kenya. There is a great need to eradicate crime to facilitate development and counter all vices that are caused by crime. Efficient management of crime requires an adequate understanding of the patterns in which crime occur to put the appropriate measures in place for crime prevention. Crime has been in existence since the beginning of time hence will remain, and one of the solutions is to identify the pattern in which it occurs to prevent or counter it effectively as it occurs. The main objective of the study was to find out how different crimes are related. The study considered a number of data mining techniques which included; clustering, specifically k-means algorithm, mapping and APRIORI algorithm to analyze how different crimes are related and how often they occur. Crime cases were found to be decreasing over the years under study and counties with a high population reported higher number of crimes as compared to those with low population. The study suggested that these crimes could be controlled by directing more resources in the highly populated counties. The study leaves a research gap where the same crime data could be analyzed using time series methods since observed crime offenses are recorded alongside the time they occur.
Stephen Mangara Wainana,
Joseph Njuguna Karomo,
Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya, International Journal of Data Science and Analysis.
Vol. 6, No. 1,
2020, pp. 20-31.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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Y. Zhao, R and data mining: Examples and case studies, Academic Press, 2012.
P.-N. Tan, M. Steinbach and. V. Kumar, "Data mining cluster analysis: basic concepts and algorithms," Introduction to data mining, pp. 487--533, 2013.
M. Kaufmann, J. Han and J. Pei, Data mining: concepts and techniques, Morgan Kaufmann, 2000.
P.-N. Tan, M. Steinbach and V. Kumar, Introduction to data mining, Pearson Education India, 2016.
M. Brown, "Data mining techniques.," Developer Works, IBM Corporation, pp. 1-16, 11 December 2012.
C. C. Yang and o. D. Ng, "Terrorism and crime related weblog social network: Link, content analysis and information visualization," in 2007 IEEE Intelligence and Security Informatics, 2007.
H. Chen, W. Chung,. J.. J. Xu, G. Wang,. Y. Qin and M. Chau, "Crime data mining: a general framework and some examples," computer, vol. 4, pp. 50--56, 2004.
D. E. Brown, "The regional crime analysis program (RECAP): a framework for mining data to catch criminals," in SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), 1998.
S. V. Nath, "Crime pattern detection using data mining," in 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, 2006.
S. Lin and. D. E. Brown, "An outlier-based data association method for linking criminal incidents," Decision Support Systems, vol. 41, no. 3, pp. 604--615, 2006.
V. Estivill-Castro and I. Lee, "Data mining techniques for autonomous exploration of large volumes of geo-referenced crime data," in Proc. of the 6th International Conference on Geocomputation, 2001.
P. L. Brantingham and P. J. Brantingham, "Environment, routine and situation: Toward a pattern theory of crime," Advances in criminological theory, vol. 5, no. 2, pp. 259--94, 1993.
A. Shafeeq and K. Hareesha, "Dynamic clustering of data with modified k-means algorithm," in Proceedings of the 2012 conference on information and computer networks, 2012.
A. Bhardwaj, A. Sharma and V. Shrivastava, "Data mining techniques and their implementation in blood bank sector--a review," International Journal of Engineering Research and Applications (IJERA), vol. 2, no. 4, pp. 1303--1309, 2012.
C. Li, N. Ding, G. Zhang and L. Li, "Association Analysis of Serial Cases Based on Apriori Algorithm," in Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence, 2019.