Interpretable models constructed by data mining algorithms from complex domains often contain relations that are statistically significant, but meaningless to a human. We propose a novel method, named Human-Machine Data Mining (HMDM), which combines human understanding and computer data mining methods to extract relations that are meaningful to the human and statistically supported with data. We term such relations credible relations. This post explains the method and provides links to the HMDM software, macroeconomic data on which the method was applied and a user study employed to evaluate HMDM.