An Improved K-Means with Artificial Bee Colony Algorithm for Clustering Crimes

Crime detection is one of the major issues in the field of criminology. In fact, criminology includes knowing the details of a crime and its intangible relations with the offender. In spite of the enormous amount of data on offenses and offenders, and the complex and intangible semantic relationships between this information, criminology has become one of the most important areas in the field of clustering. With the development of computer systems and the development of clustering algorithms, it has been possible to interpret mass data and extract knowledge from them. There are different types of attribute in the mass data set, each of which can be suitable for crime detection. By clustering, different groups of crime can be identified and also the percentage of their occurrence. In this paper, a K-Means improved by Artificial Bee Colony (ABC) algorithm is proposed for crime clustering. In the proposed model, an ABC algorithm has been used to improve cluster centers and increase the accuracy of clustering and assignment of samples to appropriate clusters. The main motivation is to exploit the search ability of ABC algorithm and to avoid the original limitation of falling into locally optimal values of the K-Means. Evaluation has done on data set with 1994 samples and 128 features. The results show that the accuracy of the proposed model is higher than K-Means, and the Purity value of the proposed model with 500 iterations is 0.943.