TY - JFULL AU - Basheer M. Al-Maqaleh and Kamal K. Bharadwaj PY - 2007/12/ TI - Genetic Programming Approach to Hierarchical Production Rule Discovery T2 - International Journal of Computer and Information Engineering SP - 3533 EP - 3537 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/10022 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 11, 2007 N2 - Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalized rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm. ER -