An Outlier-based Data Association Method for Linking Criminal Incidents

Data association is an important data-mining task and it has various applications. In crime analysis, data association means to link criminal incidents committed by the same person. It helps to discover crime patterns and catch the criminal. In this paper, we present an outlier-based data association method. An outlier score function is defined to measure the extremeness of an observation, and the data association method is developed based upon the outlier score function. We apply this method to the robbery data from Richmond, Virginia, and compare the result with a similarity-based association method. Result shows that the outlier-based data association method is promising.