A Different Approach for Pruning Micro-clusters in Data Stream Clustering

摘要

DenStream is a data stream clustering algorithm which has been widely studied due to its ability to find clusters with arbitrary shapes and dealing with noisy objects. In this paper, we propose a different approach for pruning micro-clusters in DenStream. Our proposal unlike other previously reported pruning, introduces a different way for computing the micro-cluster radii and provides new options for the pruning stage of DenStream. From our experiments over public standard datasets we conclude that our approach improves the results obtained by DenStream.