Affinity propagation is a new algorithm that takes as input measures of similarity between pairs of data points and simultaneously considers all data points as potential exemplars. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We have used affinity propagation to solve a variety of clustering problems and we found that it uniformly found clusters with much lower error than those found by other methods, and it did so in less than one-hundredth the amount of time. Because of its simplicity, general applicability, and performance. The proposition of IAPKM is inspired by combining K-Medoids and AP clustering, where AP clustering is good at finding an initial exemplar set and K-Medoids is good at modifying the current clustering result according to new arriving objects. AP algorithm can be used in this paper. Finding the similarity word sequence. Two IAP clustering algorithms, IAPKM and IAPNA, are proposed. Five popular labeled data sets and real world time series are used to evaluate the performance of IAPKM and IAPNA. Experimental results validate the effectiveness of IAPKM and IAPNA. The second one (Hierarchical WAP) is concerned with reducing the quadratic AP complexity, by applying AP on data subsets and further applying Weighted AP on the exemplars extracted from all subsets. Finally STRAP extends Hierarchical WAP to deal with changes in the data distribution.