Comprehensible knowledge-discovery in databases

Large databases are routinely being collected in science, business and medicine. A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand the data by discovering useful categories. However, to date research in data mining has not paid attention to the cognitive factors that make learned categories comprehensible. We show that one factor which influences the comprehensibility of learned models is consistency with existing knowledge and describe a learning algorithm that creates concepts with this goal in mind.