Extracting fuzzy sparse rules by Cartesian representation and clustering

Sparse rule base and interpolation have been proposed as possible solution to alleviate the geometric complexity problem of large fuzzy set. However, no formal method to extract sparse rule base is yet available. This paper combines the recently introduced Cartesian representation of membership functions and a mountain method-based clustering technique for the extraction. A case study is included to demonstrate the effectiveness of the approach