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gptkbp:instanceOf
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gptkb:algorithm
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gptkbp:application
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image segmentation
document clustering
market segmentation
vector quantization
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gptkbp:category
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unsupervised learning
cluster analysis
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gptkbp:complexity
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O(nkt)
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gptkbp:convergesWhen
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cluster assignments do not change
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gptkbp:implementedIn
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gptkb:MATLAB
gptkb:scikit-learn
R
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gptkbp:input
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set of data points
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gptkbp:introduced
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gptkb:Stuart_Lloyd
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gptkbp:introducedIn
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1957
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gptkbp:limitation
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not suitable for categorical data
assumes spherical clusters
sensitive to initial centroids
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gptkbp:measures
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Euclidean distance
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gptkbp:objective
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minimize within-cluster variance
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gptkbp:output
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set of clusters
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gptkbp:relatedTo
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gptkb:Gaussian_Mixture_Model
gptkb:DBSCAN
hierarchical clustering
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gptkbp:requires
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number of clusters (k)
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gptkbp:step
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assign points to nearest cluster center
update cluster centers
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gptkbp:supportsAlgorithm
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unsupervised learning
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gptkbp:usedIn
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gptkb:machine_learning
data mining
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gptkbp:variant
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gptkb:K-means++
gptkb:K-medoids
gptkb:Mini-batch_K-means
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gptkbp:bfsParent
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gptkb:ELKI
gptkb:DBSCAN
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gptkbp:bfsLayer
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7
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https://www.w3.org/2000/01/rdf-schema#label
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K-means
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