.png){kind=icon}
返回
Clustering data streams: Theory and practice
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2003.1198387.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and clickstreams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, While using little memory, is crucial. We describe such a Streaming algorithm that effectively clusters large data streams. We also provide empirical evidence of the algorithm's performance on synthetic and real data streams.
Keyword:
clustering
data streams
approximation algorithms
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
OAAI
OAAI分享收藏
查看更多内容