Alternatives to the k-means algorithm that find better clusterings

By Greg Hamerly, Charles Peter Elkan. International Conference on Information and Knowledge Management, 2002

562 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/6722a7a8354f1181c63d509669c2ea1b243eaaf6

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Particle swarm optimization method for image clustering (2005, methodology): “…is the quantization error, defined as J e = Nc j=1 [ ∀zp∈Cj d(z p , m j )]/|C j | N c (13) where d(z p , m j ) = N b k=1 (z pk − m jk ) 2 (14) 3.2 General Iterative Clustering Using the general form of iterative clustering used by Hamerly el al [17] [18], the steps of a clustering algorithm are: 1.”
Using the Triangle Inequality to Accelerate k-Means (2003, methodology): “Two important methods in this class are Gaussian expectation-maximization (EM) (Dempster et al., 1977) and harmonic -means ( Hamerly & Elkan, 2002 ).”
The Virtual Family—development of surface-based anatomical models of two adults and two children for dosimetric simulations (2010, methodology): “Manual segmentation was facilitated by the software iSEG, an in-house development (Neufeld 2008). iSEG provides a large number of different image processing algorithms, such as Live Wire (Schenk et al 2000), Thresholding (Suri et al 2002), Region Growing (Hamerly and Elkan 2002), Fuzzy Connectedness”
Development of a new generation of high-resolution anatomical models for medical device evaluation: the Virtual Population 3.0 (2014, methodology): “iSeg offers a large number of image processing algorithms, such as Live Wire (Schenk et al 2000), Level Sets (Suri et al 2002), Region Growing (Hamerly and Elkan 2002), Fuzzy Connectedness (Udupa and Saha 2003), Interactive Watershed Transformation (Hahn and Peitgen 2003), etc.”