Data mining methods have drawn considerable attention across diverse scientific fields. However, few applications could be found in the areas of psychological and educational measurement, and particularly pertinent to this article, in test security research. In this study, various data mining methods for detecting cheating behaviors on large-scale assessments are explored as an alternative to the traditional methods including person-fit statistics and similarity analysis. A common data set from the Handbook of Quantitative Methods for Detecting Cheating on Tests (Cizek & Wollack) was used for comparing the performance of the different methods. The results indicated that the use of data mining methods may combine multiple sources of information about test takers' performance, which may lead to higher detection rate over traditional item response and response time methods. Several recommendations, all based on our findings, are provided to practitioners.