A new Data Clustering algorithm, Affinity Propagation suffers from its quadratic complexity in function of the number of data items. Several extensions of Affinity Propagation were proposed aiming at online clustering in the data stream framework. Firstly, the case of multiply defined items, or weighted items is handled using Weighted Affinity Propagation(WAP). Secondly, Hierarchical AP achieves distributed AP and uses WAP to merge the sets of exemplars learned from subsets. Based on these two building blocks, the third algorithm performs Incremental Affinity Propagation on data streams. The paper validates the two algorithms both on benchmark and on real-world datasets. The experimental results show that the proposed approaches perform better than $K$-centers based approaches.