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Clustering. Die Clusteranalysen K-means und DBSCAN im Vergleich

Clustering. Die Clusteranalysen K-means und DBSCAN im Vergleich

          
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About the Book

Studienarbeit aus dem Jahr 2018 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 1,3, Hochschule Reutlingen, Sprache: Deutsch, Abstract: Um Clusteranalyse zu verstehen, sollte vorerst definiert werden, was unter einem "Cluster" verstanden wird. Ein Cluster ist eine Sammlung von Datenobjekten, die ähnliche Eigenschaften besitzen. Das bedeutet, dass sich die Objekte innerhalb derselben Gruppe ähneln. Sie unterscheiden sich jedoch sehr mit den Objekten in anderen Clustern. Ziel der Clusteranalyse, die auch Clustering oder Datensegmentierung genannt wird, die Objekte in eine homogene Gruppe zu teilen. Die Clusteranalyse besteht darin, Datenpunkte in eine Gruppe von Clustern oder Gruppen zu partitionieren. Um Objekte Clustern zu können, müssen diese über Proximitätsmaße (Euklidischer Abstand, Manhattan-Abstand) miteinander verglichen werden. Objekte mit geringer Distanz zueinander werden dabei in ein Cluster eingeteilt. Mittels Clusteranalyse kann man klassifizieren ohne die Klassen vorher zu kennen, dies wird auch nichtüberwachtes Lernen (Unsupervised Learning) genannt. In dem Sinne gibt es beim Clustering auch keine Trainingsdaten. Dies ist sehr verschieden von der Klassifizierung, die überwachtes Lernen erfordert. Es ist nicht sinnvoll das Clusterverfahren bei allen Datensätzen anzuwenden, denn manche Datensätze weisen keine Struktur auf und sind nur zufällig angeordnete Punkte, wo kein Cluster erkennbar ist. Die Folge wäre, dass der Datensatz falsch geclustert wird und die natürliche Datenstruktur nicht wiedergegeben werden kann. In dieser Arbeit wird auf der linken Seite der Abbildung 1 sieht man Datenpunkte, die mittels Clusteranalyse in vier Clustern eingeteilt wurden. Diese vier Cluster ähneln sich in ihren Eigenschaften. Je nach Methode können diese Objekte zu einem oder mehreren Clustern gehören. In dieser Arbeit werden beide Methoden wie K-Means und DBSCAN untersucht, angewendet und anschließend verglichen.


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Product Details
  • ISBN-13: 9783668849587
  • Publisher: Bod Third Party Titles
  • Publisher Imprint: Grin Verlag
  • Height: 210 mm
  • No of Pages: 34
  • Spine Width: 2 mm
  • Width: 148 mm
  • ISBN-10: 3668849587
  • Publisher Date: 30 Nov 2018
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Weight: 59 gr


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