Criminal Pattern Identification using Genetic Algorithm

Data mining is the process of identifying interesting patterns in data. These patterns must be meaningful in that they lead to some advantage. These patterns allow us to make prediction on new data. Clustering is a technique that group data items into classes with similar characteristics to maximize or minimize interclass similarity. For example, in the same way or differently to identify the suspects who carried out the crimes associated with gangs are different between the groups. Classification is a technique that finds common properties among different crime entities and organizes them into predefined classes. Many learning techniques can be quite complicated and typically are expressed as a set of rules or decision trees have learned that the structural descriptions. This paper presents detailed study on clustering techniques and its role on crime identification. Large and often genetic algorithms to solve complex computational problems using a variety of disciplines have given rise to many new applications. They variety of difficult practical problems in the field of powerful, high-quality solution has been found. This paper presents algorithm for crime identification.