Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
|
| gptkbp:alternativeName |
gptkb:Lloyd's_algorithm
k-means clustering |
| gptkbp:application |
image segmentation
document clustering market segmentation vector quantization |
| gptkbp:category |
partitioning method
|
| gptkbp:complexity |
O(nkt)
|
| gptkbp:convergesTo |
local minimum
|
| gptkbp:field |
gptkb:machine_learning
statistics data mining |
| https://www.w3.org/2000/01/rdf-schema#label |
k-means
|
| gptkbp:input |
set of data points
|
| gptkbp:introduced |
gptkb:Stuart_Lloyd
|
| gptkbp:introducedIn |
1957
|
| gptkbp:limitation |
sensitive to outliers
assumes spherical clusters sensitive to initial centroids requires k to be specified |
| gptkbp:measures |
Euclidean
Manhattan (less common) |
| gptkbp:notRecommendedFor |
categorical data
non-globular clusters |
| gptkbp:objective |
minimize within-cluster sum of squares
|
| gptkbp:optimizedFor |
gptkb:Lloyd's_algorithm
|
| gptkbp:output |
clusters
|
| gptkbp:popularizedBy |
gptkb:James_MacQueen
|
| gptkbp:popularizedYear |
1967
|
| gptkbp:purpose |
partitioning data into clusters
|
| gptkbp:relatedTo |
gptkb:DBSCAN
gptkb:Gaussian_mixture_model hierarchical clustering |
| gptkbp:requires |
number of clusters (k)
|
| gptkbp:software |
gptkb:MATLAB
gptkb:Spark_MLlib gptkb:scikit-learn R |
| gptkbp:step |
assign points to nearest centroid
initialize centroids repeat until convergence update centroids |
| gptkbp:supportsAlgorithm |
unsupervised learning
|
| gptkbp:uses |
Euclidean distance
|
| gptkbp:variant |
fuzzy c-means
k-medoids mini-batch k-means |
| gptkbp:bfsParent |
gptkb:Bag_of_Visual_Words
|
| gptkbp:bfsLayer |
7
|