Knowledge Extraction from Self-Organizing Map Using Minimization Entropy Principle Algorithm

Knowledge extraction using self-organizing map produced numeric values. This paper proposes knowledge extraction from self-organizing map using membership function from the minimization entropy principle algorithm to build linguistic intervals. The rough set theory was used in the rule extraction process for the minimum number of rules. The rules were in the form of linguistic "if-then" rule that user can understand easily. The benchmark data were iris database and Wisconsin breast cancer database. The experimental results received the fewer number of rules with high accuracy