Evaluation of Rule Extraction Algorithms

Abstract

For the data mining domain, the lack of explanation facilities seems to be a serious drawback for techniques based on Artificial Neural Networks, or, for that matter, any technique producing opaque models In particular, the ability to generate even limited explanations is absolutely crucial for user acceptance of such systems. Since the purpose of most data mining systems is to support decision making, the need for explanation facilities in these systems is apparent. The task for the data miner is thus to identify the complex but general relationships that are likely to carry over to production data and the explanation facility makes this easier. Also focused the quality of the extracted rules; i.e. how well the required explanation is performed. In this research some important rule extraction algorithms are discussed and identified the algorithmic complexity; i.e. how efficient the underlying rule extraction algorithm is.