中文摘要：

利用数据流的遗忘特性，应用随机投影，分层、动态地维护每个数据流的概要结构．基于该概要结构，快速计算数据流和聚类中心之间的近似距离，实现一种适合并行多数据流的K-means聚类方法．所进行的实验验证该方法的有效性．

英文摘要：

A synopsis is maintained dynamically for each data stream. The construction of the synopsis is based on random projections and it utilizes the amnesic feature of data stream. Using the synopsis, the approximate distances between streams and the cluster center can be computed fast. And an efficient online version of the classical K-means clustering algorithm is developed. The experimental results showy the method can be performed effectively with a good clustering quality.