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Implementation of K-Means Clustering Algorithm in the Crime Data Set

M. Jasmine, Dr. G. Kesavaraj

Abstract


Data mining is a most promising field to group the relative set of data. The data mining deals with many algorithms to discover interesting and useful pattern or to find new knowledge form the dataset. Clustering is the technique of grouping the dynamic data in to classes. K-.Means Clustering algorithms is one of the efficient clustering algorithm. In Data mining processing work, The first step include to collect raw data and process with key factors and to determine which algorithms is more suitable for the given data set. Criminology is the study of   identifying the crime characteristics. This paper gives an idea about implementation of K-means Algorithms for crime data set. It also takes part of crime analysis the exploration and finding crimes with criminals. The volume and complexity and their relationships of crime database is the challenge attributes. This will paper intended to create effective tool for police forces.

Keywords


Data Mining, Clustering Algorithms, k-means Clustering Algorithms, Crime Data Set

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References


MohammadReza Keyvanpour, Mostafa Javideh, Mohammad Reza Ebrahimi, “Detecting and Investigating Crime by Means Of Data Mining: A General Crime Matching Framework”, Procedia Computer Science, No. 3 (2011), 1877-0509, 2010, Elsevier, Pp. 872–880.

ChhayaChauhan and SmritiSehga, “A Review: Crime Analysis Using Data Mining Techniques And Algorithms”,International Conference on Computing, Communication and Automation (ICCCA2017),DOI : 10.1016/j.procs.2010.12.143.

Sushanta Kumar P. Krishna Reddy V. Balakista Reddy Aditya Singh, “Similarity Analysis of Legal Judgments”, COMPUTE ’11, March, Bangalore, Karnataka, India Copyright 2011 ACM 978-1-4503-0750-5/11/03, Pp. 25-26.

Donald E. Brown and Rosemary B. Oxford,” Data Mining Time Series with Applications to Crime Analysis”, 0-7803-7087-2/01, Q 2001 IEEE, Pp.1453-1458.

Deepika K.K, Smitha Vinod, “Crime analysis in India using data mining techniques”, International Journal of Engineering & Technology, Vol 7 No. (2.6), 2018 Pp.253-258.

Shiju Sathyadevan, Devan M.S, , “Crime Analysis and Prediction Using Data Mining”, IEEE, First International Conference on Networks & Soft Computing, Pp.406-412, 2014.

Sotarat Thammaboosadee and Udom Silparcha, “A Framework for Criminal Judicial Reasoning System using Data Mining Techniques”.

Ubon Thongsatapornwatana, “A Survey of Data Mining Techniques for Analyzing Crime Patterns”, Second Asian Conference on Defence Technology, IEEE (ACDT), 978-1-5090-2258-8/16/2016.

Charles V. Trappey and Ai-Che Chang,” Analysis of IP-related court decisions using e-discovery – A case study of Apotex Pty. Ltd. v Sanofi-Aventis Australia Pty. Ltd. & Ors, Int. J. Intellectual Property Management”, Vol. 9, No. 1, 2016, Inderscience Enterprises Ltd., Pp. 16-31.

Mugdha Sharma, “Z - CRIME: A Data Mining Tool for the Detection of Suspicious Criminal Activities Based on Decision Tree” IEEE,978–1–4799–4674–7/ 14, 2014.


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