Are we really discovering ''interesting'' knowledge from data?

This paper is a critical review of the literature on discovering comprehensible, interesting knowledge (or patterns) from data. The motivation for this review is that the majority of the literature focuses only on the problem of maximizing the accuracy of the discovered patterns, ignoring other important pattern-quality criteria that are user-oriented, such as comprehensibility and interestingness. The word “interesting” has been used with several different meanings in the data mining literature. In this paper interesting essentially means novel or surprising. Although comprehensibility and interestingness are considerably harder to measure in a formal way than accuracy, they seem very relevant criteria to be considered if we are serious about discovering knowledge that is not only accurate, but also useful for human decision making. The paper discusses both data-driven methods (based mainly on statistical properties of the patterns) and user-driven methods (which take into account the user’s background knowledge or believes) for discovering interesting knowledge. Data-driven methods are discussed in more detail because they are more common in the literature and are more controversial. The paper also suggests future research directions in the discovery of interesting knowledge.