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Clustering

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Data Mining

Part of the book series: Computational Intelligence ((CI))

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Zusammenfassung

Clustering ist ein unüberwachtes Lernverfahren , bei dem unmarkierte Daten Clustern zugeordnet werden. Falls die zu clusternden Daten auch Klassen zugeordnet sind, so können die erhaltenen Clusterzugehörigkeiten möglicherweise den Klassenzugehörigkeiten entsprechen. Cluster- und Klassenzugehörigkeiten können jedoch auch verschieden sein. Cluster können mathematisch mit Hilfe von Mengen, Partitionsmatrizen und/oder Cluster-Prototypen spezifiziert werden. Sequenzielles Clustering (z. B. Single-Linkage, Complete-Linkage, Average-Linkage, Ward-Methode) lässt sich einfach implementieren, hat aber einen hohen Rechenaufwand. Partitionsbasiertes Clustering kann mit scharfen, unscharfen, possibilistischen oder robusten Clustermodellen definiert werden. Clusterprototypen können verschiedene geometrische Formen annehmen (z. B. Hypersphären, Ellipsoide, Linien, Hyperebenen, Kreise oder kompliziertere Formen). Relationale Clustermodelle finden Cluster in relationalen Daten. Dabei kann auch der Kernel-Trick angewendet werden. Die Clustertendenz gibt an, ob die Daten überhaupt Cluster enthalten. Clustervaliditätsmaße quantifizieren die Güte des Clusterergebnisses und ermöglichen, die Anzahl der Cluster abzuschätzen. Auch heuristische Methoden wie die selbstorganisierende Karte können zum Clustering verwendet werden.

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Authors and Affiliations

  1. Siemens AG, München, Deutschland

    Thomas A. Runkler Prof. Dr.-Ing.

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  1. Thomas A. Runkler Prof. Dr.-Ing.
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Correspondence to Thomas A. Runkler Prof. Dr.-Ing. .

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Runkler, T. (2015). Clustering. In: Data Mining. Computational Intelligence. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-8348-2171-3_9

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