Association rules is an unsupervised learning algorithm which is sharpened for the purpose of finding patterns in data such as market basket analysis or customer preferences to create recommendation systems. Despite the fact that those are the most popular applications, this study shows how the association rules algorithms may help to analyze survey data, and namely, psychological and behavioral questionnaires for mental disorder diagnosis. The current dataset is collected from a private psychology clinic. This dataset comprised 30 samples for each of the Normal, Mania Bipolar Disorder, Depressive Bipolar Disorder, and Major Depressive Disorder categories summing up to 120 patiants. The dataset contains the 17 essential symptoms psychiatrists use to diagnose the described disorders.