Combining human analysis and machine data mining to obtain credible data relations

Can a model constructed using data mining (DM) programs be trusted? It is known that a decision-tree model can contain relations that are statistically significant, but, in reality, meaningless to a human. When the task is domain analysis, meaningless relations are problematic, since they can lead to wrong conclusions and can consequently undermine a human’s trust in DM programs. To eliminate problematic relations from the conclusions of analysis, we propose an interactive method called Human–Machine Data Mining (HMDM). The method constructs multiple models in a specific way so that a human can reexamine the relations in different contexts and, based on observed evidence, conclude which relations and models are credible—that is, both meaningful and of high quality. Based on the extracted credible relations and models, the human can construct correct overall conclusions about the domain. The method is demonstrated in two complex domains, extracting credible relations and models that indicate the segments of the higher education sector and the research and development sector that influence the economic welfare of a country. An experimental evaluation shows that the method is capable of finding important relations and models that are better in both meaning and quality than those constructed solely by the DM programs.