Skip to main content

Part of the book series: Use R! ((USE R))

The aim of unsupervised classification, or cluster analysis, is to organize observations into similar groups. Cluster analysis is a commonly used, appealing, and conceptually intuitive, statistical method. Some of its uses include market segmentation, where customers are grouped into clusters with similar attributes for targeted marketing; gene expression analysis, where genes with similar expression patterns are grouped together; and the creation of taxonomies of animals, insects, or plants. A cluster analysis results in a simplification of a dataset for two reasons: first, because the dataset can be summarized by a description of each cluster, and second, because each cluster, which is now relatively homogeneous, can be analyzed separately. Thus, it can be used to effectively reduce the size of massive amounts of data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

(2007). Cluster Analysis. In: Interactive and Dynamic Graphics for Data Analysis. Use R!. Springer, New York, NY. https://doi.org/10.1007/978-0-387-71762-3_5

Download citation

Publish with us

Policies and ethics