Mining temporal evolution of criminal behaviors

One of the recently addressed research directions focuses on the issues raised by the diffusion of highly dynamic on-line information, particularly on the problem of mining topic evolutions from news. Among several applications, risk identification and analysis may exploit mining topic evolution from news in order to support law enforcement officers in risk and threat assessment. Assimilating the concept of topic to the concept of crime typology represented by a group of "similar" criminals, it is possible to apply topic evolution mining techniques to discover evolutions of criminal behaviors over time. At this aim, we incrementally analyze streams of publicly available news about criminals (e.g. daily police reports, public court records, legal instruments) in order to identify clusters of similar criminals and represent their evolution over time. Experimental results on both real world and synthetically generated datasets prove the effectiveness of the proposed approach.