Abstract
Cluster analysis is a technique for grouping data and finding structures in data. The most common application of clustering methods is to partition a data set into clusters or classes, where similar data are assigned to the same cluster whereas dissimilar data should belong to different clusters. In real-world applications there is very often no clear boundary between clusters so that fuzzy clustering is often a good alternative to use. Membership degrees between zero and one are used in fuzzy clustering instead of crisp assignments of the data to clusters.
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Melin, P., Castillo, O. Clustering with Intelligent Techniques. In: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32378-5_8
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DOI: https://doi.org/10.1007/978-3-540-32378-5_8
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24121-8
Online ISBN: 978-3-540-32378-5
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