高维Turnstile型数据流聚类算法

An Efficient Clustering Algorithm for High Dimensional Turnstile Data Streams

现有数据流聚类算法只能处理Ti me Series和Cash Register型数据流,并且应用于高维数据流时其精度不甚理想。提出针对高维Turnstile型数据流的子空间聚类算法HT-Stream,算法对数据空间进行网格划分,在线动态维护网格单元信息,采用倾斜时间窗口存储统计信息,根据用户指定时间跨度离线输出聚类结果。基于真实数据集与仿真数据集的实验表明,算法具有良好的适用性和有效性。

Previous method only can deal with Time Series and Cash Register data stream. Moreover, the efficiency of clustering high dimensional data stream is not very satisfactory. In this paper a novel algorithm for clustering Turnstile data stream named HT-Stream is presented. HT-Stream partitions the space into grids, summarizes statistical information over data stream according to the tilted time window, and finds the clusters offline. HT-Stream can resolve high dimensional clustering problem and discover clusters with arbitrary shape. The experimental results on real datasets and synthetic datasets demonstrate promising availabilities of the approach.