Abstract
Clustering can be considered the most important unsupervised learning problem; as with every other problem of this kind, it deals with finding structure in a collection of unlabeled data. A cluster is therefore a collection of objects which are “similar” to one another and are “dissimilar” to the objects belonging to other clusters.
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© 2009 Springer-Verlag Berlin Heidelberg
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Barbakh, W.A., Wu, Y., Fyfe, C. (2009). Review of Clustering Algorithms. In: Non-Standard Parameter Adaptation for Exploratory Data Analysis. Studies in Computational Intelligence, vol 249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04005-4_2
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DOI: https://doi.org/10.1007/978-3-642-04005-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04004-7
Online ISBN: 978-3-642-04005-4
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