Abstract The existing algorithms can not solve the clustering problems for uncertain data stream from the perspective of temporal evolution. An evolutionary clustering algorithm based on affinity propagation for uncertain data stream (EAP-UStream) is presented. A concept of change rate of uncertain micro-cluster is put forward with the consideration of the influence of the varying factors caused by the procedure of online uncertain data stream forming the micro-clusters on offline clustering. The degree of similarity between the micro-clusters is measured in terms of uncertain data stream evolution. A concept of coupling degree of uncertain micro-clusters is proposed. Thus, the uncertain similarity matrix is constructed, and evolutionary clustering for uncertain data stream is realized with the idea of affinity propagation. The experimental results show the effectiveness of EAP-UStream.
About author:: 夏 聪,男,1990年生,硕士研究生,主要研究方向为数据挖掘.E-mail:xia_cong@foxmail.com. (XIA Cong, born in 1990, master student. His research inte-rests include data mining.) 陆亿红(通讯作者),女,1968年生,硕士,副教授,主要研究方向为软件理论、数据挖掘.E-mail:lyh@zjut.edu.cn. (LU Yihong(Corresponding author), born in 1968, master, associate professor. Her research interests include theory of software and data mining.)
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2016, Vol. 29 
