An Improved Dynamic Based Incremental Clustering in Affinity Propagation

Abstract- Affinity propagation based clustering algorithm will be individually placed on each and every object Specific cluster. Utilizing the subsequent clustering technique. Affinity Propagation (AP) clustering continues to be proven to work in many of clustering problems. This particular paper views the best way to utilize AP in incremental clustering problems. Firstly, we mention the problems within Incremental Affinity Propagation (IAP) clustering, then propose two techniques to solve them. Correspondingly, two IAP clustering algorithms are usually proposed. Five popular labeled data sets, real-world time series as well as a video are employed test the performance associated with IAPKM and IAPNA. Standard AP clustering is usually implemented to produce benchmark performance. Experimental results show that IAPKM and IAPNA is capable of doing comparable clustering performance together with standard AP clustering on each of the data sets. Meanwhile, the time cost is actually dramatically reduced within IAPKM and IAPNA. The two effectiveness and also the efficiency make IAPKM and IAPNA capable of being well utilized in incremental clustering tasks