A fuzzy clustering algorithm to detect criminals without prior information

Crime analysis has been widely studied, but problem of identifying conspirators through communication network analysis is still not well resolved. In this paper, we proposed a fuzzy clustering algorithm to detect hidden criminals from topic network, which took no use of individuals' prior identity information. We first built up a local suspicion calculation from nodes' neighboring information (node and edge); and then with global information, we employed the fuzzy k-means clustering algorithm, and made the membership to suspicious group as the global suspicion degree. Experiments showed it works well on identification: known suspects gained relative high values and known innocents got relative low values.