Abstract: Most data mining and induction learning methods only rely on discrete attributes.So, continuous attributes must be discretized.This paper presents a new data discretization method for continuous attributes based on information entropy, namely IED.It measures the similarity of intervals by using information entropy and considers the effect of the discrete interval size on discretization results.This method synthetically takes into account the independence betweem the merged intervals and target class.Experimental results show that IED can yield more classification accuracy by implementing Naïve-bayes.