基于划分与层次方法的混合聚类算法  

Hybrid clustering algorithm based on partitioning and hierarchical method

作　　者：张丽[1] 崔卫东[2] 邱保志[1] 

机构地区：[1]郑州大学信息工程学院,郑州450001 [2]商丘师范学院计算机科学系,河南商丘476000

出　　处：《计算机工程与应用》2010年第16期127-129,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60673087);郑州大学骨干教师项目~~

摘　　要：为了更好地实现聚类,在汲取传统的划分算法、层次算法特性的基础上,提出了一种新的基于划分和层次的混合聚类算法(MPH),该算法将聚类的过程分为分裂和合并两个阶段,在分裂阶段反复采用k-means算法,将数据集划分为多个同质的子簇,在合并阶段采用凝聚的层次聚类算法。实验表明,该算法能够发现任意形状、任意大小的聚类,并且对噪声点不敏感。In order to obtain better clustering results,this paper proposes a new hybrid clustering algorithm based on traditional partitioning and hierarchical methods（shorted for MPH）.The algorithm divides the clustering process into two phases：Splitting and merging.During the splitting process,MPH divides the dataset into a number of sub-clusters by repeatedly using k-means algorithm;and during the merging process,it clusters by agglomerative hierarchical methods.The experimental results show that the algorithm is significantly effective in discovering clusters of arbitrary shapes,sizes,and it is not sensitive to the noises.

关 键 词：聚类 相似度 相对互连度 相对接近度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]