Identifying Flexible Structured Premises for Mining Concise Fuzzy Knowledge

Data mining attains growing importance to ease the knowledge-acquisition bottleneck. This chapter discusses the issue of extracting, from accumulated data, a compact fuzzy rule base, to reach concise yet highly generalizing knowledge. For this purpose, we establish flexible structured premises of rules, allowing for not only canonical AND combinations of input fuzzy sets but also OR connectives of linguistic terms as well as incomplete compositions of input variables in the premise constitution. The later two forms of premises are beneficial for rule number reduction, as they achieve bigger coverage of the input space compared with the first premise form. A genetic-based search algorithm is utilized to explore optimal premise structure in combination with parameters of fuzzy set membership functions. Simulation results on several data sets are given to demonstrate the merits and characteristics of the presented method.