Standard Article

Cluster Analysis of Subjects, Hierarchical Methods

  1. Brian S. Everitt

Published Online: 15 JUL 2005

DOI: 10.1002/0470011815.b2a13008

Encyclopedia of Biostatistics

Encyclopedia of Biostatistics

How to Cite

Everitt, B. S. 2005. Cluster Analysis of Subjects, Hierarchical Methods. Encyclopedia of Biostatistics. 2.

Author Information

  1. Institute of Psychiatry, London, UK

Publication History

  1. Published Online: 15 JUL 2005


Hierarchical cluster classification involves partitioning data into a series of groups. The first group consists of n single-member “clusters”, the last consists of a single group with all n individuals. Hierarchical cluster classification could be represented by a diagram known as a dendrogram. Properties and problems of hierarchical clustering techniques are described. The two major types of algorithms that have been used to produce hierarchical classifications are agglomerative and divisive. Careful validation of solutions is a clear requirement in any clustering exercise. An example to illustrate hierarchical methods is presented.


  • hierarchical classification;
  • dendrogram;
  • rooted terminally-labeled;
  • weighted tree;
  • agglomerative algorithms;
  • divisive algorithms