Streaming Data Clustering by Incremental Affinity Propagation

This paper introdused based on clustering. Affinity Propagation (AP) clustering has been successfully used in a lot of clustering problems. This paper considers how to apply AP in incremental clustering problems in streams of data. First, we point out the difficulties in Incremental Affinity Propagation (IAP) clustering, and then propose two strategies to solve them. Two IAP clustering algorithms are proposed. They are IAP clustering based on K-Medoids (IAPKM) and IAP clustering based on Nearest Neighbor Assignment (IAPNA). Traditional AP clustering is also implemented to provide benchmark performance. Experimental results show that IAPKM and IAPNA can achieve comparable clustering performance with traditional AP clustering on all the data sets. Popular labeled data sets, real world time series and a video, datastreams are used to test the performance of IAPKM and IAPNA. Both the effectiveness and the efficiency make IAPKM and IAPNA able to be well used in incremental clustering tasks.