In analysis of complex domains, data mining methods that construct interpretable models frequently construct relations that are statistically significant, but meaningless to a human. We propose a novel method, named Human-Machine Data Mining (HMDM) that combines human understanding and computer data mining methods to extract credible relations, which are at the same time meaningful to the human and statistically supported with data. The method defines a procedure and a toolbox that human uses in interactive and iterative manner to direct computer search towards those parts of the search space with credible relations. Based on credible relations, the human can construct correct conclusions about the domain. HMDM was successfully applied on the problems from macroeconomic, demographic and web genre classification domains.
Searching for credible relations through interactive data mining – Information Sciences, 2014