ICTACT Journal on Soft Computing

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A Violent Crime Analysis using Fuzzy C-Means Clustering Approach


Affiliations
1 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, India
     

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Clustering Techniques are the most significant method of grouping data points based on certain similarity. There are two ways in clustering techniques, namely hard and soft clustering. Traditional clustering approaches include grouping of each object to only one cluster. However, there are some cases that each object may belong to multiple partitions. Normally, healthcare and educational data encompass multiple clustering. Such multiple partitioning can be accomplished using overlapping clustering and soft or fuzzy clustering approaches. In this work, Fuzzy C-Means clustering model is applied for multiple clustering based on crime rates. The proposed multiple clustering model is evaluated using USArrests dataset and the results are useful to predict the high possibility of crime incidence by visualizing the crime analysis in various states in US.

Keywords

Crime Analysis, Hard and Soft Clustering, Fuzzy C-Means Clustering, Overlapping Clustering.
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  • S. Sathyadevan, M.S. Devan and S.S. Gangadharan, “Crime Analysis and Prediction Using Data Mining”, Proceedings of IEEE 1st International Conference on Networks and Soft Computing, pp. 406-412, 2014.

  • J.C. Bezdek, R. Ehrlich and W. Full, “FCM: The Fuzzy C-Means Clustering Algorithm”, Computer and Geosciences, Vol. 10, No. 2-3, pp. 191-203, 1984.

  • L. Xiang Ning, S. Tie Lin , W. Su Ya, L. Li Yi , S. Lei and L. Guang Lan, “Intelligent Diagnosis of the Solder Bumps Defects using Fuzzy C-Means Algorithm with the Weighted Coefficients”, Science China Technological Sciences, Vol. 58, No. 10, pp. 1689-1695, 2015.

  • E. Rashedi and A. Mirzaei, “A Novel Multi-Clustering method for Hierarchical Clusterings, Based on Boosting”, Proceedings of 19th IEEE Iranian Conference on Electrical Engineering, pp. 1-4, 2011.

  • H.T. Zheng, H. Chen, S.Q. Gong, “A Frequent Term-Based Multiple Clustering Approach for Text Documents”, Proceedings of Asia-Pacific Web Conference on Web Technologies and Applications, pp. 602-609, 2014.

  • M. Sreedevi, A. Harsha Vardhan Reddy and C.H. Venakata Sai Krishna Reddy, “Review on Crime Analysis and Prediction using Data Mining Techniques”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 7, No. 4, pp. 3360-3369, 2018.

  • A. Bharati and R.A.K. Sarvanaguru, “Crime Prediction and Analysis using Machine Learning”, International Research Journal of Engineering and Technology, Vol. 5, No. 9, pp. 1037-1042, 2018.

  • S.N. Aarathi, N. Gayathri, R. Indraja , S. Srividhya and J. Kayalvizhi, “Crime Analysis and Prediction using Big Data”, International Journal of Pure and Applied Mathematics, Vol. 119, No. 12, pp. 207-211, 2018.

  • H.B.F. David and A. Suruliandi, “Survey on Crime Analysis and Prediction using Data Mining Techniques”, ICTACT Journal on Soft Computing, Vol. 7, No. 3, pp. 1459-1466, 2017.

  • USArrests, Available at: https://www.kaggle.com/deepakg/usarrests

  • Datasets distributed with R Git Source Tree, Available at: https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/USArrests

  • J.C. Dunn, “A Fuzzy Relative of the ISODATA Process and its use IN Detecting Compact Well-Separated Clusters”, Journal of Cybernetics, Vol. 3, No. 3, pp. 32-57, 1973.

  • J.C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algoritms”, Plenum Press, 1981.

  • A Tutorial on Clustering Algorithms, Available at: https://sites.google.com/site/dataclusteringalgorithms/fuzzy-c-means-clustering-algorithm.

  • K. Taha and P.D. Yoo, “SIIMCO: A Forensic Investigation Tool for Identifying the Influential Members of a Criminal Organization”, IEEE Transactions on Information Forensics and Security, Vol. 11, No. 4, pp. 811-822, 2016.

  • M. Gupta, B. Chandra and M.P. Gupta, “Crime Data Mining for Indian Police Information System”, Journal of Crime, Vol. 2, No. 6, pp. 43-54, 2006.

  • R. Kiani, S. Mahdavi and A. Keshavarzi, “Analysis and Prediction of Crimes by Clustering and Classification”, International Journal of Advanced Research in Artificial Intelligence, Vol. 4, No. 8, pp. 11-17, 2015.

  • K. Bogahawatte and S. Adikari, “Intelligent Criminal Identification System”, Proceedings of 8th IEEE International Conference on Computer Science and Education, pp. 633-638, 2013.

  • J. Agarwal, R. Nagpal and R. Sehgal, “Crime Analysis using K-Means Clustering”, International Journal of Computer Applications, Vol. 83, No. 4, pp. 1-4, 2013.

  • S.V. Nath, “Crime Pattern Detection using Data Mining”, Proceedings of IEEE International Conference on Web Intelligence and Intelligent Agent Technology, pp. 1-4, 2006.

  • D. J. Hand, H. Mannila and P. Smyth, “Principles of Data Mining”, MIT Press, 2001.

  • D. E. Brown, “The Regional Crime Analysis Program (RECAP): A Framework for Mining Data to Catch Criminals”, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp. 2848-2853, 1998.

  • R. Iqbal, M.A.A. Murad, A. Mustapha, P.H.S. Panahy and N. Khanahmadliravi, “An Experimental Study of Classification Algorithms for Crime Prediction”, Indian Journal of Science and Technology, Vol. 6, No. 3, pp. 4219-4225, 2013.

  • M. Sharma, “Z-Crime: A Data Mining Tool for the Detection of Suspicious Criminal Activities based on the Decision Tree”, Proceedings of International Conference on Data Mining and Intelligent Computing, pp. 1-6, 2014.

  • 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. 37, No. 4, pp. 50-56, 2004.


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  • A Violent Crime Analysis using Fuzzy C-Means Clustering Approach

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Authors

M. Premasundari
Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, India
C. Yamini
Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, India

Abstract


Clustering Techniques are the most significant method of grouping data points based on certain similarity. There are two ways in clustering techniques, namely hard and soft clustering. Traditional clustering approaches include grouping of each object to only one cluster. However, there are some cases that each object may belong to multiple partitions. Normally, healthcare and educational data encompass multiple clustering. Such multiple partitioning can be accomplished using overlapping clustering and soft or fuzzy clustering approaches. In this work, Fuzzy C-Means clustering model is applied for multiple clustering based on crime rates. The proposed multiple clustering model is evaluated using USArrests dataset and the results are useful to predict the high possibility of crime incidence by visualizing the crime analysis in various states in US.

Keywords


Crime Analysis, Hard and Soft Clustering, Fuzzy C-Means Clustering, Overlapping Clustering.

References