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Clustering

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Data Analytics

Abstract

Clustering is unsupervised learning that assigns labels to objects in unlabeled data. When clustering is performed on data that do have physical classes, the clusters may or may not correspond with the physical classes. Cluster partitions may be mathematically represented by sets, partition matrices, and/or cluster prototypes. Sequential clustering (single linkage, complete linkage, average linkage, Ward’s method, etc.) is easily implemented but computationally expensive. Partitional clustering can be based on hard, fuzzy, possibilistic, or noise clustering models. Cluster prototypes can take many forms such as hyperspheric, ellipsoidal, linear, circles, or more complex shapes. Relational clustering models find clusters in relational data. Complex relational clusters can be found by kernelization. Cluster tendency assessment finds out if the data contain clusters at all, and cluster validity measures help to identify an appropriate number of clusters. Clustering can also be done by heuristic methods such as the self-organizing map.

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  1. Thomas A. Runkler

    Present address: , Ina-Seidel-Bogen 15, 81929, München, Germany

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    © 2012 Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden

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    Runkler, T. (2012). Clustering. In: Data Analytics. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-8348-2589-6_9

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    • DOI: https://doi.org/10.1007/978-3-8348-2589-6_9

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