Cluster Analysis of Subjects, Hierarchical Methods
Published Online: 15 JUL 2005
Copyright © 2005 John Wiley & Sons, Ltd
Encyclopedia of Biostatistics
How to Cite
Everitt, B. S. 2005. Cluster Analysis of Subjects, Hierarchical Methods. Encyclopedia of Biostatistics. 2.
- 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;
- rooted terminally-labeled;
- weighted tree;
- agglomerative algorithms;
- divisive algorithms