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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).
About the author
The Author is a Lecturer of Computer Science at Covenant University, Ota, Ogun State, Nigeria. She is an internationally published scholar. Her research interests include designing of Expert Systems and application of Data Mining with soft computing techniques in solving real life problems in different fields.
