This book tackles two concerns of knowledge engineers in designing and developing a fuzzy rule-based expert system (FES). First is to acquire a knowledge-base that emulates human perception of application domain concept in order to avoid sharp boundary problems. Second is the need for modelling a comprehensive fuzzy rule-based expert system which eliminates redundant rules in order to solve the problem of rule-base unwieldiness and provide for knowledge-base instant updates. This book introduces Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition. In doing this, the Apriori-like Fuzzy Association Rule Mining algorithm was adopted. Adopting FARME-D automated knowledge acquisition in modelling fuzzy expert system eliminates redundant rules and save memory usage. The rules generated based on expert-driven approach correspond to human perception of the application domain as compared to data-driven approach. Also, the integration of FARME-D approach to standard fuzzy expert system architecture provides for knowledge-base instant updates and resulted in a novel architecture called Fuzzy Association Rule Mining Expert System (FARMES).