Data Stream Clustering Algorithm Based on Affinity Propagation and Density
Yang Li, Bai Hong Tan · Advanced materials research · 2011
Data stream clustering is an important issue in data steam mining. In the field of data stream analysis, conventional methods seem not quite efficient. Because neither they can adapt to the dynamic environment of data stream, nor the mining models and result s can meet users’ needs. An affinity propagation and grid based clustering method is proposed to effectively address the problem. The algorithm applies AP clustering on each partition of the data stream to generate reference point set, and subsequently density based clustering is applied to these reference points to get the clustering result of each periods. Theoretic analysis and experimental results show it is effective and efficient.