Abstract
If an assignment of classes to patterns is not evident, then unsupervised learning methods are often helpful. Methods of finding clusters in a multidimensional pattern space are used to find natural classes in a data set. Such methods are not trivial and always contain heuristic and arbitrary elements. Subjective parameters are necessary to control the size, shape and number of clusters for a certain problem. Different representations of the data often give different clusters.
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© 1980 Springer-Verlag Berlin Heidelberg
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Varmuza, K. (1980). Clustering Methods. In: Pattern Recognition in Chemistry. Lecture Notes in Chemistry, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-93155-0_7
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DOI: https://doi.org/10.1007/978-3-642-93155-0_7
Publisher Name: Springer, Berlin, Heidelberg
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