Identifying crimes using data mining techniques
Prevention and identification of crime has always been one of the important and basic issues in human life that has been applied in different methods througho7ut history. Due to development of information and communication technologies and the establishment of comprehensive information systems in the police force and recording of criminal information in databases, the use of data mining techniques and knowledge discovery techniques to analyze and root out crimes such as theft is one the necessities of police and judicial systems. The main purpose of this research is to identify crimes using data mining techniques that can be identified and discovered by using present crimes, CDCI and data mining algorithms, crime patterns. In the proposed model, the crime detection is analyzed by using K-Assay clustering, which regularly creates two polygon clusters, based on characteristics of similar features. Crime confirmation of the results is performed using WEKA. WEKA chooses 93.62% and 93.99% accuracy in forming two clusters of crime by using crime features.