Abstract
In the current times where human safety is threatened by man-made and natural calamities, surveillance systems have gained immense importance. But, even in presence of high definition (HD) security cameras and manpower to monitor the live feed 24/7, room for missing important information due to human error exists. In addition to that, employing an adequate number of people for the job is not always feasible either. The solution lies in a system that allows automated surveillance through classification and other data mining techniques that can be used for extraction of useful information out of these inputs. In this research, a data mining-based framework has been proposed for surveillance. The research includes interpretation of data from different networks using hybrid data mining technique. In order to show the validity of the proposed hybrid data mining technique, an online data set containing network of a suspicious group has been utilized and main leaders of network has been identified.