Knowledge about criminal networks has important implications for crime investigation and the anti-terrorism campaign. However, lack of advanced, automated techniques has limited law enforcement and intelligence agencies' ability to combat crime by discovering structural patterns in criminal networks. In this research we used the concept space approach, clustering technology, social network analysis measures and approaches, and multidimensional scaling methods for automatic extraction, analysis, and visualization of criminal networks and their structural patterns. We conducted a case study with crime investigators from the Tucson Police Department. They validated the structural patterns discovered from gang and narcotics criminal enterprises. The results showed that the approaches we proposed could detect subgroups, central members, and between-group interaction patterns correctly most of the time. Moreover, our system could extract the overall structure for a network that might be useful in the development of effective disruptive strategies for criminal networks.