Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
unsupervised learning algorithm |
| gptkbp:category |
partitioning method
|
| gptkbp:complexity |
O(nkt)
|
| gptkbp:convergesWhen |
assignments do not change
centroids do not change |
| gptkbp:field |
gptkb:artificial_intelligence
computer science data science statistics |
| https://www.w3.org/2000/01/rdf-schema#label |
K-means algorithm
|
| gptkbp:implementedIn |
gptkb:MATLAB
gptkb:Spark_MLlib gptkb:scikit-learn R |
| gptkbp:input |
set of data points
|
| gptkbp:limitation |
sensitive to outliers
assumes spherical clusters sensitive to initial centroids requires k to be specified |
| gptkbp:measures |
Euclidean distance
|
| gptkbp:openSource |
gptkb:Apache_Spark
gptkb:scikit-learn R |
| gptkbp:output |
cluster centroids
set of clusters |
| gptkbp:popularizedBy |
gptkb:James_MacQueen
1967 |
| gptkbp:proposedBy |
gptkb:Stuart_Lloyd
1957 |
| gptkbp:reduces |
within-cluster sum of squares
|
| gptkbp:relatedTo |
gptkb:Gaussian_Mixture_Model
gptkb:DBSCAN hierarchical clustering Mean Shift |
| gptkbp:requires |
number of clusters (k)
|
| gptkbp:step |
assign points to nearest centroid
repeat until convergence update centroids initialize k centroids |
| gptkbp:usedIn |
gptkb:machine_learning
pattern recognition data mining image segmentation vector quantization |
| gptkbp:variant |
gptkb:Fuzzy_C-means
gptkb:K-medoids gptkb:Mini-batch_K-means |
| gptkbp:bfsParent |
gptkb:EM_algorithm
|
| gptkbp:bfsLayer |
7
|