Mining data streams using clustering

被引：0
|
作者：
Lu, YH [1 ]
Huang, Y [1 ]
机构：
[1] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310032, Peoples R China
来源：
Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9 | 2005年
关键词：
data stream; minining data streams; K-Means algorithm; statistical grid-based algorithm; regression analysis;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to this reason, traditional data mining approach is replaced by systems that are able to mine continuous, high-volume, open-ended data streams as they arrive. In this paper, we survey three data stream clustering algorithms, namely clustering data streams using K-Means, statistical grid-based, and regression analysis. We compare and contrast these techniques as well.
引用
收藏
页码：2079 / 2083
页数：5
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