K-means

GPTKB entity

Statements (36)
Predicate Object
gptkbp:instanceOf gptkb:algorithm
gptkbp:application image segmentation
document clustering
market segmentation
vector quantization
gptkbp:category unsupervised learning
cluster analysis
gptkbp:complexity O(nkt)
gptkbp:convergesWhen cluster assignments do not change
https://www.w3.org/2000/01/rdf-schema#label K-means
gptkbp:implementedIn gptkb:MATLAB
gptkb:scikit-learn
R
gptkbp:input set of data points
gptkbp:introduced gptkb:Stuart_Lloyd
gptkbp:introducedIn 1957
gptkbp:limitation not suitable for categorical data
assumes spherical clusters
sensitive to initial centroids
gptkbp:measures Euclidean distance
gptkbp:objective minimize within-cluster variance
gptkbp:output set of clusters
gptkbp:relatedTo gptkb:Gaussian_Mixture_Model
gptkb:DBSCAN
hierarchical clustering
gptkbp:requires number of clusters (k)
gptkbp:step assign points to nearest cluster center
update cluster centers
gptkbp:supportsAlgorithm unsupervised learning
gptkbp:usedIn gptkb:machine_learning
data mining
gptkbp:variant gptkb:K-means++
gptkb:K-medoids
gptkb:Mini-batch_K-means
gptkbp:bfsParent gptkb:DBSCAN
gptkbp:bfsLayer 6