Subgroup discovery is the task of identifying subgroups that show the most unusual statistical (distributional) characteristics with respect to a given target variable, at the intersection of predictive and descriptive induction. Redundancy and lack of rule interpretability constitute the major challenges in subgroup discovery today. We address these two issues by constrained latent Dirichlet allocation (LDA) to identify co-occurring feature values (descriptions) for subgroup rule search, obtaining a less redundant and more diverse rule set. Latent Dirichlet Allocation, as a topic modeling approach, is able to identify diverse topics, from which the rules can be derived. The resulting rules are less redundant and can also be interpreted by the corresponding topic. Experimental results on six benchmark datasets show that the presented approach provides rule sets with better rule redundancy and diversity compared to those of four existing algorithms. One unique and interesting advantage of the proposed method is that it can categorize rules by topics as well as the assignment of a probability to each feature value of a discovered rule, which can be used in the interpretation of the results.