Abstract
As we saw earlier in the visualizations provided by methods like PCA and SOM, it is often interesting to look for structure, or groupings, in the data. However, these methods do not explicitly define clusters; that is left to the pattern recognition capabilities of the scientist studying the plot.
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
Notes
- 1.
We can only be friends if all our friends are friends of both of us.
- 2.
Especially for the BIC value, one often sees the negative form so that maximization will lead to an optimal model. This is also the definition by (Schwarz 1978).
- 3.
To be more precise, model-based hierarchical clustering (Fraley 1998).
- 4.
Metaphor from Adrian Raftery.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Wehrens, R. (2020). Clustering. In: Chemometrics with R. Use R!. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62027-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-662-62027-4_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-62026-7
Online ISBN: 978-3-662-62027-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)