Cluster analysis is the generic name for a variety of mathematical methods for appraising similarities among a set of objects, where each object is described by measurements made on its attributes. The input to a cluster analysis is a data matrix having t columns, one for each object, and n rows, one for each attribute. The (i, j)th element of the data matrix is the measurement of the ith attribute for the jth object. The output from a cluster analysis identifies groups of similar objects called clusters. A cluster may contain as few as one object, because an object is similar to itself.
Applications of cluster analysis are widespread because the need to assess similarities and dissimilarities among objects is basic to fields as diverse as agriculture, geology, market research, medicine, sociology, and zoology. For example, a hydrologist considers as the objects a set of streams, and for attributes describes each stream with a list of water quality measures. A cluster analysis of the...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References and Further Reading
Aldenderfer MS, Blashfield RK (1984) Cluster analysis. Sage, Beverly Hills
Everitt B (1993) Cluster analysis. E. Arnold, London
Romesburg HC (2004) Cluster analysis for researchers. Lulu.com, North Carolina
Sneath PHA, Sokal RR (1973) Numerical taxonomy: the principles and practice of numerical classification. W. H. Freeman, San Francisco
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this entry
Cite this entry
Romesburg, H. (2011). Cluster Analysis: An Introduction. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_310
Download citation
DOI: https://doi.org/10.1007/978-3-642-04898-2_310
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04897-5
Online ISBN: 978-3-642-04898-2
eBook Packages: Mathematics and StatisticsReference Module Computer Science and Engineering