{"title":"Discovery of Quantified Hierarchical Production Rules from Large Set of Discovered Rules","authors":"Tamanna Siddiqui, M. Afshar Alam","volume":25,"journal":"International Journal of Computer and Information Engineering","pagesStart":162,"pagesEnd":169,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/11102","abstract":"
Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form: Decision If < condition> Generality