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

页码：2079 / 2083

页数：5

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