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Part of the book series: Studies in Computational Intelligence ((SCI,volume 249))

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

  • eBook Packages: EngineeringEngineering (R0)

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