Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (16): 127-129.DOI: 10.3778/j.issn.1002-8331.2010.16.037

• 数据库、信号与信息处理 • Previous Articles     Next Articles

Hybrid clustering algorithm based on partitioning and hierarchical method

ZHANG Li1,CUI Wei-dong2,QIU Bao-zhi1   

  1. 1.Information Engineering College,Zhengzhou University,Zhengzhou 450001,China
    2.Department of Computer Science,Shangqiu Normal University,Shangqiu,Henan 476000,China
  • Received:2008-11-17 Revised:2009-02-02 Online:2010-06-01 Published:2010-06-01
  • Contact: ZHANG Li


张 丽1,崔卫东2,邱保志1   

  1. 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
  • 通讯作者: 张 丽

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.

Key words: clustering, similarity, relative inter-connectivity, relative closeness

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

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