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基于划分与层次方法的混合聚类算法
引用本文:张丽,崔卫东,邱保志. 基于划分与层次方法的混合聚类算法[J]. 计算机工程与应用, 2010, 46(16): 127-129. DOI: 10.3778/j.issn.1002-8331.2010.16.037
作者姓名:张丽  崔卫东  邱保志
作者单位: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
基金项目:国家自然科学基金,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。

关 键 词:聚类  相似度  相对互连度  相对接近度  
收稿时间:2008-11-17
修稿时间:2009-2-2 

Hybrid clustering algorithm based on partitioning and hierarchical method
ZHANG Li,CUI Wei-dong,QIU Bao-zhi. Hybrid clustering algorithm based on partitioning and hierarchical method[J]. Computer Engineering and Applications, 2010, 46(16): 127-129. DOI: 10.3778/j.issn.1002-8331.2010.16.037
Authors:ZHANG Li  CUI Wei-dong  QIU Bao-zhi
Affiliation:1.Information Engineering College,Zhengzhou University,Zhengzhou 450001,China 2.Department of Computer Science,Shangqiu Normal University,Shangqiu,Henan 476000,China
Abstract: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.
Keywords:clustering  similarity  relative inter-connectivity  relative closeness
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