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gptkbp:instanceOf
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gptkb:algorithm
gptkb:unsupervised_learning_algorithm
|
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gptkbp:category
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partitioning method
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gptkbp:complexity
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O(nkt)
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gptkbp:convergesWhen
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assignments do not change
centroids do not change
|
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gptkbp:field
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gptkb:artificial_intelligence
computer science
data science
statistics
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gptkbp:implementedIn
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gptkb:MATLAB
gptkb:Spark_MLlib
gptkb:scikit-learn
R
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gptkbp:input
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set of data points
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gptkbp:limitation
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sensitive to outliers
assumes spherical clusters
sensitive to initial centroids
requires k to be specified
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gptkbp:measures
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Euclidean distance
|
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gptkbp:openSource
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gptkb:Apache_Spark
gptkb:scikit-learn
R
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gptkbp:output
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cluster centroids
set of clusters
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gptkbp:popularizedBy
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gptkb:James_MacQueen
1967
|
|
gptkbp:proposedBy
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gptkb:Stuart_Lloyd
1957
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gptkbp:reduces
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within-cluster sum of squares
|
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gptkbp:relatedTo
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gptkb:Gaussian_Mixture_Model
gptkb:DBSCAN
hierarchical clustering
Mean Shift
|
|
gptkbp:requires
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number of clusters (k)
|
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gptkbp:step
|
assign points to nearest centroid
repeat until convergence
update centroids
initialize k centroids
|
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gptkbp:usedIn
|
gptkb:machine_learning
pattern recognition
data mining
image segmentation
vector quantization
|
|
gptkbp:variant
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gptkb:Fuzzy_C-means
gptkb:K-medoids
gptkb:Mini-batch_K-means
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gptkbp:bfsParent
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gptkb:Expectation-Maximization_Algorithm
gptkb:EM_algorithm
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gptkbp:bfsLayer
|
9
|
|
https://www.w3.org/2000/01/rdf-schema#label
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K-means algorithm
|