Comprehensible Knowledge-Discovery in Databases
Michael J. Pazzani · 1997
Large databases are routinely being collected in science, business and medicines. 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 that influences the comprehensibility of learned models is consistency with existing knowledge and describe a learning algorithm that creates concepts with this goal in mind. Introduction Knowledge-discovery in databases is a field whose goal is to extract usable knowledge from a collection of data. It draws upon methods in statistics, signal processing, pattern recognition, information theory, machine learning, and neural networks to produce models that provide insight into data. Such models are expected to be accurate and are further expected to be compre...