摘要
本文提出了一种基于自适应网格划分的数据流聚类算法。通过采用网格的自适应划分,对传统的基于密度网格的数据流聚类算法,以均衡划分网格的方法进行改进,使网格的划分更加合理,减少硬性划分对结果可能造成的影响,提高了硬性划分边界的精度。同时采用剪枝方法,减少了算法的执行时间。最后,通过实验验证了该算法的有效性。
This paper proposes a data stream clustering algorithm based on adaptive grid partitioning. By using adaptive grid partitioning to improve the traditional methed of dividing grids in a balanced method, we make the grid division more reasonable and reduce the impact on the result, which improves the precision of grid partitioning. Using a pruning method to ruduce the algorithm＇s execution time is effective. Finally, the experimental results verify the effectiveness of the proposed algorithm.
出处
《计算机工程与科学》
CSCD
北大核心
2011年第10期149-153,共5页
Computer Engineering & Science
关键词
数据流
聚类
滑动窗口
网格
data stream
clustering
sliding window
grid
