Abstract
Cluster analysis, which is the most well-known example of unsupervised learning, is a very popular tool for analyzing unstructured multivariate data. Within the data-mining community, cluster analysis is also known as data segmentation, and within the machine-learning community, it is also known as class discovery. The methodology consists of various algorithms each of which seeks to organize a given data set into homogeneous subgroups, or “clusters.” There is no guarantee that more than one such group can be found; however, in any practical application, the underlying hypothesis is that the data form a heterogeneous set that should separate into natural groups familiar to the domain experts.
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© 2013 Springer Science+Business Media New York
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Izenman, A.J. (2013). Cluster Analysis. In: Modern Multivariate Statistical Techniques. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-78189-1_12
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DOI: https://doi.org/10.1007/978-0-387-78189-1_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-78188-4
Online ISBN: 978-0-387-78189-1
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