Intuitionistic Fuzzy K-Modes （IFKM） algorithm adopts the simple 0-1 matching similarity measure in clustering process， which can not effectively describe the similarity of data objects in class， and fails to reflect the contribution of different attributes in clustering process. In addition， IFKM algorithm directly determines the classes of data objects according to the intuitionistic fuzzy membership matrix in each iteration of clustering， and do not give full play to the role of intuitionistic fuzziness idea. In order to solve these two problems， an Iterative IFKM （IIFKM） algorithm was proposed. Firstly， a weighted similarity measure of intuitionistic fuzzy membership degree was defined based on Intuitionistic Fuzzy Entropy（IFE） and Intuitionistic Fuzzy Set （IFS）. Secondly， the intuitionistic fuzzy membership matrix was used as iterative information in the whole clustering process， so that the intuitionistic fuzziness idea in the algorithm was fully reflected. Experimental results on 5 datasets from UCI database show that compared with IFKM algorithm， the proposed IIFKM algorithm can improve the accuracy and recall by 7%-11%， and can also improve the precision to some degree.