A Supervised Learning Approach for Criminal Identification

Data mining tools are best approach for Criminal identification based on characteristic and nature of crime. In this book, we have proposed a supervised approach for identifying the suspected list of criminal¿s using similarity measure and K-Medoids cluster algorithm. K-Medoids clustering algorithm groups the more closely related crimes as an individual group and each group will have unique set of features. The unique features set is used for identification of criminal using similarity measure algorithms based on distance measure. The proposed system has two phase, training and testing phase. In this approach, we have trained the proposed system with supervised data set with collected crime information from various places of Tamil Nadu through online available data. In the testing phase, first identify the cluster closest to the test crime by using K-Medoids clustering algorithm and then identify the suspected criminal list using similarity measure. The initial stage of implementation and analysis of the proposed scheme provides good results and high accuracy. The proposed scheme is compared with related K-Means clustering algorithm with same set of training and test instances.