摘要
提出了一种基于网格生长树的微粒群聚类算法。算法利用网格和密度阈值去除数据集中的孤立点,从网格集中随机地选取种子点,以基于密度距离作为判断生长方向及分类的依据,以网格生长树的大小作为聚类目标函数。引入微粒群算法确定最终的聚类结果。测试表明,基于网格生长树的微粒群聚类算法对于大规模形状复杂非重叠的数据是可行且有效的。
A PSO-clustering algorithm based on Gird-Propagating Tree（PSO-GPT） is presented in this paper.This algorithm uses the gird and density threshold method to remove isolated points of data set,selects the initial seed points in propagating tree randomly,judges vegetal direction and executes sort according to the density-based distance,calculates cluster aim function by the propagating tree value.PSO algorithm is employed to determine the final clustering results in the new algorithm.Experimental results show that the proposed method is feasible and effective for large-scale complex shape and non-duplication data. ）
出处
《计算机工程与应用》
CSCD
北大核心
2008年第32期143-147,共5页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60674104)。
关键词
聚类算法
网格生长树
微粒群算法
clustering algorithm
Gird-Propagating Tree
Particle Swarm Optimization（PSO
