A Survey of Data Mining Techniques for Crime Detection

Abstract

In large datasets, data mining is one of the most powerful ways of knowledge extraction or we can say it is one of the best approaches to detect underlying relationships among data with the help of machine learning and artificial intelligence techniques. Crime Detection is one of the hot topics in data mining where different patterns of criminology are identified. It includes variety of steps, starting from identification of crime characterization till detection of crime pattern. For this purpose, various crime detection techniques have been discussed in literature. In this paper, we have selected widely adapted data mining techniques that are specifically used for crime detection. The analytical study is presented with an extraction in form of strengths and weakness of each technique. Each technique is specific to its use, for example to identify the social ties and roles of criminal in any network, Social Network Analysis techniques is best suited because of its degree, density and centrality of nodes. This survey would serve as a helping guide to researchers to get state of the art crime detection techniques in data mining along with pros and cons.

Keywords

• Data mining
• Crime detection
• Classification
• Clustering
• Association
• Prediction
• Constraint
• Association rules