Data mining methods offer a powerful tool for psychologists to capture complex relations such as interaction and nonlinear effects without prior specification. However, interpreting and integrating information from data mining models can be challenging. The current research proposes a strategy to identify nonlinear and interaction effects by using a deductive data mining approach that in essence consists of comparing increasingly complex data mining models. The proposed approach is applied to three empirical data sets with details on how to interpret each step and model comparison, along with simulations providing a proof of concept. Annotated example code is also provided. Ultimately, the proposed deductive data mining approach provides a novel perspective on exploring interactions and nonlinear effects with the goal of model explanation and confirmation. Limitations of the current approach and future directions are also considered.