Abstract: Crime prevention and detection become an important trend in crime and a very challenging to solve crimes. The crime data previously stored from various sources have a tendency to increase steadily. As a consequence, the supervision and analysis with huge data are very tricky and complex. To solve the problems, data mining techniques employ many learning algorithms to extort hidden knowledge from huge volume of data. Data mining is data analyzing techniques to find patterns and trends in crimes. It can help solve the crimes more speedily and also can help alert the criminal detection automatically. Clustering is a data analyzing technique in unsupervised type. This technique is used to divide the same data into the same group and the different data into the other group. For the simple and effective clustering techniques, there are several algorithms such as K-means clustering. This approach is supervised learning scheme that used to dispense objects to one of many pre-determined categories. The algorithms of categorization have been widely applied to the numerous problems that include many various applications. Crime are characterized which change over time and increase continuously. The changing and increasing of crime direct to the issues of understanding the crime behavior, crime predicting, precise detection and managing large volumes of data obtained from various sources.
Keywords: Data Mining, Clustering, K-means clustering, Association Rule Mining.