| 高维Turnstile型数据流聚类算法 |
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| 引用本文： | 周晓云,张净,孙志挥. 高维Turnstile型数据流聚类算法[J]. 计算机科学, 2006, 33(11): 14-17 |
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| 作者姓名： | 周晓云 张净 孙志挥 |
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| 作者单位： | 东南大学计算机科学与工程系,南京,210096;东南大学计算机科学与工程系,南京,210096;江苏大学电气信息工程学院,镇江,212001 |
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| 基金项目： | 国家自然科学基金;高等学校博士学科点专项科研项目;江苏省自然科学基金 |
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| 摘 要： | 现有数据流聚类算法只能处理Time 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。
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| 关 键 词： | 数据流 子空间聚类 高维 倾斜时间窗口 |
An Efficient Clustering Algorithm for High Dimensional Turnstile Data Streams |
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| ZHOU Xiao-Yun,ZHANG Jing,SUN Zhi-Hui. An Efficient Clustering Algorithm for High Dimensional Turnstile Data Streams[J]. Computer Science, 2006, 33(11): 14-17 |
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| Authors: | ZHOU Xiao-Yun ZHANG Jing SUN Zhi-Hui |
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| Affiliation: | Department of Computer Science and Engineering, Southeast University, Nanjing 210096; College of Electronic and Information Engineering, Jiangsu University, Zhenjiang 212001 |
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| Abstract: | 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. |
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| Keywords: | Data stream Subspace clustering High dimension Tilted time windows |
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