关键词: 数据挖掘, 数据流, 聚类, 滑动窗口

Abstract: The clustering of data streams is an important problem for clustering analysis.In order to address the data streams with varying speed，an efficient data streams clustering algorithm over dynamic sliding windows is proposed，which based on the two-phased framework.In the online component，the novel micro-cluster feature is introduced to store the important statistical information of data streams.Through computing the distances from data points to the center of each micro-cluster，and adjusting the sizes of sliding windows，the corresponding clustering features are maintained dynamically.In the offline component，by employing the mean values of the micro-clusters in online component，k-means algorithm is adopted to generate the final clustering results.Experimental results show that this approach has higher clustering purity and better scalability.

Key words: data mining, data streams, clustering, sliding windows