2021Unpublished venueRequires access

Crime Data Analysis Using Data Mining Techniques for Crime Detection and Prevention

Revatthy Krishnmurthy, Hariharan Krishnamurthy, M. Sakthivel

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Abstract

Law enforcement agencies are facing crime complexities in recent decades due to paucity of hi-tech methods to h andle the abundant crime data. Careful investigation of crime analysis is must. Crime hotspots are detected by various data mining techniques and tools. In this paper, various types of crime analysis are used for classifying the crime types and one of the clustering methods namely 10-fold attribute subset selection method which is used for clustering the subsets and finally crimes are detected and prevented in future. This helps law enforcers to proceed with the unsolved crimes in future.

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What this paper is about

Law enforcement agencies are facing crime complexities in recent decades due to paucity of hi-tech methods to h andle the abundant crime data. Careful investigation of crime analysis is must. Crime hotspots are detected by various data mining techniques and tools. In this paper, various types of crime analysis are used for classifying the crime types and one of the clustering methods namely 10-fold attribute subset selection method which is used for clustering the subsets and finally crimes are detected and prevented in future. This helps law enforcers to proceed with the unsolved crimes in future.

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Available abstract

Law enforcement agencies are facing crime complexities in recent decades due to paucity of hi-tech methods to h andle the abundant crime data. Careful investigation of crime analysis is must. Crime hotspots are detected by various data mining techniques and tools. In this paper, various types of crime analysis are used for classifying the crime types and one of the clustering methods namely 10-fold attribute subset selection method which is used for clustering the subsets and finally crimes are detected and prevented in future. This helps law enforcers to proceed with the unsolved crimes in future.

Key concepts: Crime analysis, Law enforcement, Cluster analysis, Criminology, Data mining, Computer science, Crime prevention, Data science

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