We introduce Bayesian Ising mixture models as a novel approach to infer associations between binary variables. This method combines the strengths of classic methods, such as Ising models and multivariate Bernoulli mixture models. This framework is not only effective in fitting sparse contingency tables but also provides interpretable results. We examine the conditions required for the identifiability of the Ising mixture model, and develop Bayesian sampling algorithms for implementation. In the end we provide simulation experiments and real-data applications to showcase the efficacy of our proposed approach.