21. Appendix B: Clustering and Discriminant Analysis

  1. Ingvar Eidhammer,
  2. Harald Barsnes,
  3. Geir Egil Eide and
  4. Lennart Martens

Published Online: 10 JAN 2013

DOI: 10.1002/9781118494042.ch21

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

How to Cite

Eidhammer, I., Barsnes, H., Eide, G. E. and Martens, L. (2013) Appendix B: Clustering and Discriminant Analysis, in Computational and Statistical Methods for Protein Quantification by Mass Spectrometry, John Wiley & Sons Ltd, Oxford, UK. doi: 10.1002/9781118494042.ch21

Publication History

  1. Published Online: 10 JAN 2013
  2. Published Print: 4 JAN 2013

ISBN Information

Print ISBN: 9781119964001

Online ISBN: 9781118494042

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Keywords:

  • hierarchical clustering;
  • k-means clustering;
  • linear discriminant analysis (LDA);
  • sequential clustering

Summary

The field of clustering and discriminant analysis is large, and it is employed in various scientific fields such as statistics, machine learning, and pattern recognition. Both clustering and discriminant analysis start with a set of objects, often called an example set or a training set. The main task in clustering is to divide the objects under consideration into reasonable classes, such that a structural ordering of the objects can be achieved. There are a lot of different clustering approaches, of which the most common are: sequential clustering; hierarchical clustering; and k-means clustering. Discriminant analysis considers objects where it is known which class each object belongs to. This appendix section discusses the most common ways of handling missing data, and the linear discriminant analysis (LDA) using original features.

Controlled Vocabulary Terms

hierarchical clustering; k-means clustering; linear discriminant analysis