A novel approach for document clustering to criminal identification by using ABK-means algorithm

The important role of digital forensics is to improve the investigation of criminal activities that involve gather, to preserve, analyze, digital devices and provide technical and scientific evidence, and to provide the important documentation to authorities. To automatically group the retrieved documents into a list of meaningful categories different clustering techniques can be used. In last few decades many researchers research is projected to analyze the criminal with that of crime. It is seen that there is a large amount of increase in the crime rate due to the gap between the optimal usage of investigation and technologies. Because of this there are many new opportunities for the development of new methodologies and techniques in the field of crime investigation using the methods based on data mining, forensic, image processing, and social mining. Document clustering involves descriptor and descriptors extraction. In this paper, presents a model using new methodology for evaluation of document clustering of criminal database by using k-means clustering technique. This model clusters the criminal data basing on the type crime.