Crime Prediction using K-means AlgorithmJournal
: GRD Journal for Engineering (Vol.2, No. 5)
Publication Date: 2017-04-01
Authors : Vineet Jain; Ayush Bhatia; Vaibhav Arora; Yogesh Sharma;
Page : 206-209
Keywords : Crime Prediction; K-Means; Clustering; Data Mining; Crime Prone Areas;
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Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. About 10% of the criminals commit about 50% of the crimes. Even though we cannot predict who all may be the victims of crime but can predict the place that has probability for its occurrence. K-means algorithm is done by partitioning data into groups based on their means. K-means algorithm has an extension called expectation - maximization algorithm where we partition the data based on their parameters. This easy to implement data mining framework works with the geospatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. This system can also be used for the Indian crime departments for reducing the crime and solving the crimes with less time.
Citation: Vineet Jain, Maharaja Agrasen Institute of Technology; Ayush Bhatia ,Maharaja Agrasen Institute of Technology; Vaibhav Arora ,Maharaja Agrasen Institute of Technology; Yogesh Sharma ,Maharaja Agrasen Institute of Technology. "Crime Prediction using K-means Algorithm." Global Research and Development Journal For Engineering 25 2017: 206 - 209.
Last modified: 2017-05-03 00:28:24