Machine learning has evolved and expanded as an interdisciplinary research method for educational sciences. However, cheating detection of test collusion among multiple examinees or sets of examinees with unusual answer patterns using machine learning techniques has remained relatively unexplored. This study investigates collusion on multiple-choice tests by introducing feature representation methodologies and machine learning algorithms that can be jointly used as a promising method; they can be used not only to detect individual examinees involved in the collusion but also to evaluate test collusion with or without the groups of potentially dishonest examinees identified a priori. Furthermore, using small-sample examples, the visual detection procedures of the current study were articulated to help identify questionable item response groups and simultaneously focus on the specific individuals providing anomalous answers.