A fuzzy clustering algorithm to detect criminals without prior information

Changjun Fan, Kaiming Xiao, Bao-Xin Xiu, Guodong Lv

Abstract: 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.