A comparative study of efficient initialization methods for the k-means clustering algorithm

► K-means is the most widely used partitional clustering algorithm. ► k-means is highly sensitive to the selection of the initial centers. ► We present an overview of k-means initialization methods (IMs). ► We then compare eight commonly used linear time IMs. ► We demonstrate that popular IMs often...

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Veröffentlicht in:	Expert systems with applications Jg. 40; H. 1; S. 200 - 210
Hauptverfasser:	Celebi, M. Emre, Kingravi, Hassan A., Vela, Patricio A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Ltd 01.01.2013 Elsevier
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Cluster analysis Cluster center initialization Clustering Clusters Collection Computational efficiency Computer science; control theory; systems Data processing. List processing. Character string processing Exact sciences and technology Expert systems k-means Memory organisation. Data processing Partitional clustering Software Statistical tests Sum of squared error criterion Theoretical computing k-means Sum of squared error criterion Partitional clustering Cluster center initialization Initialization Gradient Non parametric test Statistical analysis Cluster Linear time K means algorithm Recommendation Search algorithm Gradient descent Experimental result Efficiency Classification Descent method Database Linear complexity Alternative method Occupation time Time complexity
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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