Telecommunications Network Laboratory, National Digital Switching System Engineering and Technological Research and Development Center, Zhengzhou 450002
Abstract To improve the accuracy and timeliness of data stream clustering, an efficient data stream clustering algorithm is proposed with temporal characteristics and affinity propagation methods (TCAPStream).The algorithm merges the newly detected class mode into clustering model by using the improved WAP algorithm, meanwhile, the temporal evolution characteristic of the data stream is reflected by using micro-cluster temporal density. Besides, the online dynamic deletion mechanism is proposed to maintain the micro-clusters. It makes the algorithm model reflecting both temporal and distribution characteristics of data stream to obtain more accurate clustering results. The experimental results show that the proposed algorithm not only has good clustering effect in several artificial datasets and real datasets, but also has good flexibility and extensibility.
2014, Vol. 27 
