Abstract
Cluster analysis is an unsupervised pattern recognition frequently used in biology, where large amounts of data must often be classified. Hierarchical agglomerative approaches, the most commonly used techniques in biology, are described in this chapter. Particular attention is put on techniques for validating the optimal cluster number and the clustering quality.
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Further Reading
Theodoris, S., Koutroumbas, K. (2003) Pattern Recognition. Academic Press, Amsterdam.
Corrigan, M. S. (2007) Pattern Recognition in Biology. Nova Science, Lancaster.
Kaufman, L., Rousseeuw, P. J. (2005) Finding Groups in Data: An Introduction to Cluster Analysis. Wiley-Interscience, New York.
Romesburg, H. C. (2004) Cluster Analysis for researchers. Lulu Press, North Carolina.
Everitt, N. S., Landau, S., Leese, M. (2001) Cluster Analysis. Hodder Arnold Publications, Oxford.
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Carugo, O. (2010). Clustering Criteria and Algorithms. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 609. Humana Press. https://doi.org/10.1007/978-1-60327-241-4_11
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DOI: https://doi.org/10.1007/978-1-60327-241-4_11
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Publisher Name: Humana Press
Print ISBN: 978-1-60327-240-7
Online ISBN: 978-1-60327-241-4
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