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Law enforcement agencies use modern crime analysis software to solve and prevent crimes. The development of crime analysis tools requires access to incident-level crime data (criminals’ IDs, time and place of incidents, etc). However, obtaining such data is very hard in practice, since the crime data is confidential. On the other hand, crime analysis usually processes aggregate-level information, such as frequency of crimes occurring over a particular geography, rather than processing the incident-level data. In this paper, a decision-making method is proposed to infer the closely related incidents using clustering with hybrid similarity metrics. The incident-level crime data is artificially generated by using a parametric GIS model. The motivation for this approach is that in general crime analysis methods do not require fully realistic data set in order to develop and test decision making algorithms. The motivation behind using hybrid metrics is that the incidentlevel crime data includes both numeric and categorical information with unstable feature vector lengths. In order to evaluate the proposed method, a novel performance metric, called Crime Driven Casual Relation Performance (CDCRP), is introduced. The results show that the proposed method well decides the causally related incidents.