@Reference(authors="S. D. Lee, B. Kao, R. Cheng", title="Reducing UK-means to K-means", booktitle="ICDM Data Mining Workshops, 2007", url="https://doi.org/10.1109/ICDMW.2007.40", bibkey="DBLP:conf/icdm/LeeKC07") public class CKMeans extends CenterOfMassMetaClustering<Clustering<KMeansModel>>
This is a baseline reference method, that computes the center of mass (centroid) of each object, then runs k-means on this.
This algorithm was introduced as CK-Means in:
S. D. Lee, B. Kao, R. Cheng
Reducing UK-means to K-means
ICDM Data Mining Workshops, 2007
and was shown to be equivalent to UK-Means.
In summary, the expected distance used by UK-Means can be decomposed using Steiner/König-Huygens into the sum of squares between the centroids, and the sum of squared deviations within the uncertain object itself. This last term, however, is constant.
|Modifier and Type||Class and Description|
Parameterization class, based on k-means.
|Modifier and Type||Field and Description|
|Constructor and Description|
Constructor that uses an arbitrary k-means algorithm.
Constructor that uses Lloyd's k-means algorithm.
|Modifier and Type||Method and Description|
Get the (STATIC) logger for this class.
getInputTypeRestriction, run, runClusteringAlgorithm
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
private static final Logging LOG
public CKMeans(KMeans<?,KMeansModel> kmeans)
kmeans- K-Means algorithm to use.
public CKMeans(NumberVectorDistanceFunction<? super NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization initializer)
distanceFunction- Distance functions for centers
k- K parameter
maxiter- Maximum number of iterations
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