Abstract
In this chapter, we explore quantitative approaches to clustering, the process of identifying groups of like objects. This grouping is based upon similarities or differences as measured by the characters that the objects possess. Clustering is closely related to the process of classification, which is assigning objects into predetermined categories. This assignment to a category is also based upon the particular states of the characters associated with that object. We discussed classification in the last chapter and will say a little more at the end of this chapter. For more extensive discussions of clustering and classification, see Dunn and Everitt (1982), Everitt and Dunn (2001), and Johnson and Wichern (2002).
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References
Dunn G, Everitt BS (1982) Introduction to Mathematical Taxonomy. Cambridge: Cambridge University Press.
Everitt BS, Dunn G (2001) Applied Multivariate Data Analysis (2nd edition). Oxford: Oxford University Press.
Hartigan J, Wong M (1979) A K-means clustering algorithm. Applied Statistics, 28:100–108.
Johnson RA, Wichern DW (2002) Applied Multivariate Statistical Analysis. Englewood Cliffs, NJ: Prentice-Hall.
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(2005). Similarity, Distance, and Clustering. In: Computational Genome Analysis. Springer, New York, NY. https://doi.org/10.1007/0-387-28807-4_10
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DOI: https://doi.org/10.1007/0-387-28807-4_10
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98785-9
Online ISBN: 978-0-387-28807-9
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