摘要
针对数据流具有数据量无限且流速快的特点,将高斯混合密度降解模型应用于数据流聚类问题,在数据流中找出有效的高斯分量,并且合并相等的高斯分量.通过采用真实数据进行实验的结果表明,此方法能够有效解决数据流的聚类问题.
The characteristics of data stream are infinite data and quick stream speed. Faced to these problems, Gaussian mixture density decomposition is used to solve data stream clustering problem. The paper finds effective Gaussian components and merges them . The experimental results from real data show that the algorithm is very effective to solve data stream clustering.
出处
《江南大学学报（自然科学版）》
CAS
2007年第6期891-894,共4页
Joural of Jiangnan University (Natural Science Edition)
关键词
聚类
数据流
高斯混合降解模型
clustering
data stream
Gaussian mixture density decomposition
