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.