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A genetic algorithm approach to automated discovery of hierarchical production rules with fuzzy hierarchy

Automated discovery of hierarchical structures in large datasets has been an active research area in the recent years. A concept hierarchy can facilitate mining knowledge at multiple level of abstraction. Crisp description for a concept hierarchy usually cannot represent human knowledge completely and practically. This paper focuses on the issue of mining generalized rules with fuzzy hierarchical structure using Genetic Algorithm (GA) to knowledge discovery. A fuzzy subsumption relation and suitable fitness functions are proposed. Appropriate genetic operators are proposed for the suggested encoding. Finally, Hierarchical Production Rules with Fuzzy Hierarchy (HPRFH) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed approach.