On the role of interpretability in fuzzy data mining
Data Mining, a central step in the border overall process of Knowledge Discovery from Databases, concerns with discovering useful properties, called patterns, from data. Understandability is an essential - yet rarely tackled - feature that makes resulting patterns accessible by end users. In this paper we argue that the adoption of Fuzzy Logic for Data Mining can improve understandability of derived patterns. Indeed, Fuzzy Logic is able to represent concepts in a "human-centric" way. Hence, Data Mining methods based on Fuzzy Logic for Data Mining is not enough to achieve understandability. This paper describes and comments a number of isseus that need to be addressed to provide for understandable patterns. A careful consideration of all issues may end up in a systemic methodology to discover comprehensible knowledge from data.
interpretability fuzzy data mining comprehensibility postulate