A framework for generation of rules from decision tree and decision table

Within an organization knowledge is present in various form, may be in minds of workers or in documented form. Knowledge is one the most precious resource of an organization. Every organization wishes to preserve and fully utilize its knowledge has various representation schemes such as frames, scripts, lists, rules, decision trees and decision tables etc. Knowledge in the form of rules is easy to understand and fast to extract and implement as compared to decision tree and decision table. The authors propose a framework to automate the construction of rules from decision tree and decision table. The knowledge in decision table can be converted to set of rules by transformation of decision table to decision tree or directly to set of rules. The knowledge in decision tree can be directly transformed to set of rules. The set of rules are then refined and optimized, and the existing knowledge base is updated through these rules.