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K

k - Variable in class elki.clustering.AbstractProjectedClustering

The number of clusters to find

k - Variable in class elki.clustering.AbstractProjectedClustering.Par

The number of clusters to find

k - Variable in class elki.clustering.CFSFDP

Number of clusters to find.

k - Variable in class elki.clustering.CFSFDP.Par

Number of clusters to find.

k - Variable in class elki.clustering.correlation.COPAC.Settings

Neighborhood size.

k - Variable in class elki.clustering.correlation.ERiC.Settings

Neighborhood size.

k - Variable in class elki.clustering.correlation.HiCO

Number of neighbors to query.

k - Variable in class elki.clustering.correlation.HiCO.Par

Number of neighbors to query.

k - Variable in class elki.clustering.dbscan.LSDBC.Par

kNN parameter.

k - Variable in class elki.clustering.em.BetulaGMM

Number of cluster centers to initialize.

k - Variable in class elki.clustering.em.BetulaGMM.Par

k Parameter.

k - Variable in class elki.clustering.em.EM

Number of clusters

k - Variable in class elki.clustering.em.EM.Par

Number of clusters.

k - Variable in class elki.clustering.em.KDTreeEM

number of models

k - Variable in class elki.clustering.em.KDTreeEM.Par

Number of clusters.

k - Variable in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans

Number of cluster centers to initialize.

k - Variable in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans.Par

k Parameter.

k - Variable in class elki.clustering.kcenter.GreedyKCenter

number of clusters

k - Variable in class elki.clustering.kcenter.GreedyKCenter.Par

number of clusters

k - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance

Number of clusters.

k - Variable in class elki.clustering.kmeans.AbstractKMeans

Number of cluster centers to initialize.

k - Variable in class elki.clustering.kmeans.AbstractKMeans.Par

k Parameter.

k - Variable in class elki.clustering.kmeans.BisectingKMeans

Desired value of k.

k - Variable in class elki.clustering.kmeans.FuzzyCMeans

Number of clusters

k - Variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Number of clusters.

k - Variable in class elki.clustering.kmedoids.AlternatingKMedoids

Number of clusters to find.

k - Variable in class elki.clustering.kmedoids.AlternatingKMedoids.Par

The number of clusters to find

k - Variable in class elki.clustering.kmedoids.CLARANS

Number of clusters to find.

k - Variable in class elki.clustering.kmedoids.CLARANS.Par

Number of cluster centers to find.

k - Variable in class elki.clustering.kmedoids.PAM

The number of clusters to produce.

k - Variable in class elki.clustering.kmedoids.PAM.Par

The number of clusters to produce.

k - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering

Number of neighbors to use for bandwidth.

k - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

Number of neighbors to use for bandwidth.

k - Variable in class elki.clustering.subspace.HiSC

The number of nearest neighbors considered to determine the preference vector.

k - Variable in class elki.clustering.subspace.HiSC.Par

The number of nearest neighbors considered to determine the preference vector.

k_i - Variable in class elki.clustering.AbstractProjectedClustering

Multiplier for the number of initial seeds

k_i - Variable in class elki.clustering.AbstractProjectedClustering.Par

Multiplier for the number of initial seeds

K_I_ID - Static variable in class elki.clustering.AbstractProjectedClustering.Par

Parameter to specify the multiplier for the initial number of seeds, must be an integer greater than 0.

K_ID - Static variable in class elki.clustering.AbstractProjectedClustering.Par

Parameter to specify the number of clusters to find, must be an integer greater than 0.

K_ID - Static variable in class elki.clustering.CFSFDP.Par

Number of clusters parameter

K_ID - Static variable in class elki.clustering.correlation.COPAC.Par

Size for the kNN neighborhood used in the PCA step of COPAC.

K_ID - Static variable in class elki.clustering.correlation.ERiC.Par

Size for the kNN neighborhood used in the PCA step of ERiC.

K_ID - Static variable in class elki.clustering.correlation.HiCO.Par

Optional parameter to specify the number of nearest neighbors considered in the PCA, must be an integer greater than 0.

K_ID - Static variable in class elki.clustering.dbscan.LSDBC.Par

Parameter for neighborhood size.

K_ID - Static variable in class elki.clustering.em.EM.Par

Parameter to specify the number of clusters to find.

K_ID - Static variable in class elki.clustering.em.KDTreeEM.Par

Parameter to specify the number of clusters to find.

K_ID - Static variable in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Par

The number of clusters to extract.

K_ID - Static variable in class elki.clustering.kcenter.GreedyKCenter.Par

Parameter to specify the number of clusters

K_ID - Static variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Parameter to specify the number of clusters to find, must be an integer greater than 0.

K_ID - Static variable in interface elki.clustering.kmeans.KMeans

Parameter to specify the number of clusters to find, must be an integer greater than 0.

K_ID - Static variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

Number of neighbors for bandwidth estimation.

K_ID - Static variable in class elki.clustering.subspace.HiSC.Par

The number of nearest neighbors considered to determine the preference vector.

k_max - Variable in class elki.clustering.kmeans.XMeans

Effective number of clusters, minimum and maximum.

k_min - Variable in class elki.clustering.kmeans.XMeans

Effective number of clusters, minimum and maximum.

kappa - Variable in class elki.clustering.correlation.FourC.Settings

Kappa penalty parameter, to punish deviation in low-variance Eigenvectors.

kappa - Variable in class elki.clustering.subspace.PreDeCon.Settings

The kappa penality factor for deviations in preferred dimensions.

KAPPA_DEFAULT - Static variable in class elki.clustering.correlation.FourC.Settings.Par

Default for kappa parameter.

KAPPA_DEFAULT - Static variable in class elki.clustering.subspace.PreDeCon.Settings.Par

Default for kappa parameter.

KAPPA_ID - Static variable in class elki.clustering.correlation.FourC.Settings.Par

Parameter Kappa: penalty for deviations in preferred dimensions.

KAPPA_ID - Static variable in class elki.clustering.subspace.PreDeCon.Settings.Par

Parameter Kappa: penalty for deviations in preferred dimensions.

KDNode(Relation<? extends NumberVector>, DBIDArrayIter, int, int) - Constructor for class elki.clustering.kmeans.KDTreePruningKMeans.KDNode

Constructor.

KDTree(Relation<? extends NumberVector>, ArrayModifiableDBIDs, int, int, double[], double) - Constructor for class elki.clustering.em.KDTreeEM.KDTree

Constructor for a KDTree with statistics needed for KDTreeEM calculation.

KDTreeEM - Class in elki.clustering.em

Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), calculated on a kd-tree.

KDTreeEM(int, double, double, double, double, TextbookMultivariateGaussianModelFactory, int, int, boolean, boolean) - Constructor for class elki.clustering.em.KDTreeEM

Constructor.

KDTreeEM.KDTree - Class in elki.clustering.em

KDTree class with the statistics needed for EM clustering.

KDTreeEM.Par - Class in elki.clustering.em

Parameterization class.

KDTreeFilteringKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Filtering or "blacklisting" K-means with k-d-tree acceleration.

KDTreeFilteringKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, KDTreePruningKMeans.Split, int) - Constructor for class elki.clustering.kmeans.KDTreeFilteringKMeans

Constructor.

KDTreeFilteringKMeans.Instance - Class in elki.clustering.kmeans

Inner instance, storing state for a single data set.

KDTreeFilteringKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Parameterization class.

KDTreePruningKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Pruning K-means with k-d-tree acceleration.

KDTreePruningKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, KDTreePruningKMeans.Split, int) - Constructor for class elki.clustering.kmeans.KDTreePruningKMeans

Constructor.

KDTreePruningKMeans.Instance - Class in elki.clustering.kmeans

Inner instance, storing state for a single data set.

KDTreePruningKMeans.KDNode - Class in elki.clustering.kmeans

Node of the k-d-tree used internally.

KDTreePruningKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Parameterization class.

KDTreePruningKMeans.Split - Enum in elki.clustering.kmeans

Splitting strategies for constructing the k-d-tree.

keepfirst - Variable in class elki.clustering.kmeans.initialization.FarthestPoints.Par

Flag for discarding the first object chosen.

KEEPFIRST_ID - Static variable in class elki.clustering.kmeans.initialization.FarthestPoints.Par

Option ID to control the handling of the first object chosen.

keepmed - Variable in class elki.clustering.kmedoids.CLARA

Keep the previous medoids in the sample (see page 145).

keepmed - Variable in class elki.clustering.kmedoids.CLARA.Par

Keep the previous medoids in the sample.

keepmed - Variable in class elki.clustering.kmedoids.FastCLARA

Keep the previous medoids in the sample (see page 145).

keepmed - Variable in class elki.clustering.kmedoids.FastCLARA.Par

Keep the previous medoids in the sample.

keepmed - Variable in class elki.clustering.kmedoids.FasterCLARA

Keep the previous medoids in the sample (see page 145).

keepmed - Variable in class elki.clustering.kmedoids.FasterCLARA.Par

Keep the previous medoids in the sample.

keepsteep - Variable in class elki.clustering.optics.OPTICSXi

Keep the steep areas, for visualization.

keepsteep - Variable in class elki.clustering.optics.OPTICSXi.Par

KEEPSTEEP_ID - Static variable in class elki.clustering.optics.OPTICSXi.Par

Parameter to keep the steep areas

kernel - Variable in class elki.clustering.NaiveMeanShiftClustering

Density estimation kernel.

kernel - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering

Kernel density function.

kernel - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

Kernel density function.

KERNEL_ID - Static variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

Kernel function.

key - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance

Key for statistics logging.

key - Variable in class elki.evaluation.clustering.internal.CIndex

Key for logging statistics.

key - Variable in class elki.evaluation.clustering.internal.ClusterRadius

Key for logging statistics.

key - Variable in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau

Key for logging statistics.

key - Variable in class elki.evaluation.clustering.internal.DaviesBouldinIndex

Key for logging statistics.

key - Variable in class elki.evaluation.clustering.internal.PBMIndex

Key for logging statistics.

key - Static variable in class elki.evaluation.clustering.internal.Silhouette

Key for logging statistics.

key - Variable in class elki.evaluation.clustering.internal.SimplifiedSilhouette

Key for logging statistics.

key - Variable in class elki.evaluation.clustering.internal.SquaredErrors

Key for logging statistics.

key - Variable in class elki.evaluation.clustering.internal.VarianceRatioCriterion

Key for logging statistics.

KEY - Static variable in class elki.clustering.em.EM

Key for statistics logging.

KEY - Static variable in class elki.clustering.kmeans.FuzzyCMeans

Key for statistics logging.

KEY - Static variable in class elki.clustering.kmedoids.AlternatingKMedoids

Key for statistics logging.

KEY - Static variable in class elki.clustering.kmedoids.EagerPAM

Key for loggers.

KEY - Static variable in class elki.clustering.kmedoids.FasterPAM

Key for statistics logging.

KEY - Static variable in class elki.clustering.kmedoids.FastPAM

Key for statistics logging.

KEY - Static variable in class elki.clustering.kmedoids.FastPAM1

Key for statistics logging.

KEY - Static variable in class elki.clustering.kmedoids.ReynoldsPAM

Key for statistics logging.

KMC2 - Class in elki.clustering.kmeans.initialization

K-MC² initialization

KMC2(int, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.KMC2

Constructor.

KMC2.Instance - Class in elki.clustering.kmeans.initialization

Abstract instance implementing the weight handling.

KMC2.Par - Class in elki.clustering.kmeans.initialization

Parameterization class.

kmeans(double[][], ClusteringFeature[], int[], int[]) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans

Perform k-means clustering.

kmeans(ArrayList<? extends ClusterFeature>, int[], int[], CFTree<?>) - Method in class elki.clustering.kmeans.BetulaLloydKMeans

Perform k-means clustering.

KMeans<V extends elki.data.NumberVector,M extends Model> - Interface in elki.clustering.kmeans

Some constants and options shared among kmeans family algorithms.

KMeansInitialization - Interface in elki.clustering.kmeans.initialization

Interface for initializing K-Means

KMeansMinusMinus<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

k-means--: A Unified Approach to Clustering and Outlier Detection.

KMeansMinusMinus(NumberVectorDistance<? super V>, int, int, KMeansInitialization, double, boolean) - Constructor for class elki.clustering.kmeans.KMeansMinusMinus

Constructor.

KMeansMinusMinus.Instance - Class in elki.clustering.kmeans

Inner instance, storing state for a single data set.

KMeansMinusMinus.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Parameterization class.

KMeansModel - Class in elki.data.model

Trivial subclass of the MeanModel that indicates the clustering to be produced by k-means (so the Voronoi cell visualization is sensible).

KMeansModel(double[], double) - Constructor for class elki.data.model.KMeansModel

Constructor with mean.

KMeansPlusPlus<O> - Class in elki.clustering.kmeans.initialization

K-Means++ initialization for k-means.

KMeansPlusPlus(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.KMeansPlusPlus

Constructor.

KMeansPlusPlus.Instance<T> - Class in elki.clustering.kmeans.initialization

Abstract instance implementing the weight handling.

KMeansPlusPlus.MedoidsInstance - Class in elki.clustering.kmeans.initialization

Instance for k-medoids.

KMeansPlusPlus.NumberVectorInstance - Class in elki.clustering.kmeans.initialization

Instance for k-means, number vector based.

KMeansPlusPlus.Par<V> - Class in elki.clustering.kmeans.initialization

Parameterization class.

KMeansProcessor<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.parallel

Parallel k-means implementation.

KMeansProcessor(Relation<V>, NumberVectorDistance<? super V>, WritableIntegerDataStore, double[]) - Constructor for class elki.clustering.kmeans.parallel.KMeansProcessor

Constructor.

KMeansProcessor.Instance<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.parallel

Instance to process part of the data set, for a single iteration.

KMeansQualityMeasure<O extends elki.data.NumberVector> - Interface in elki.clustering.kmeans.quality

Interface for computing the quality of a K-Means clustering.

KMediansLloyd<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see PAM instead).

KMediansLloyd(NumberVectorDistance<? super V>, int, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.KMediansLloyd

Constructor.

KMediansLloyd.Instance - Class in elki.clustering.kmeans

Inner instance, storing state for a single data set.

KMediansLloyd.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Parameterization class.

KMedoidsClustering<O> - Interface in elki.clustering.kmedoids

Interface for clustering algorithms that produce medoids.

KMedoidsInitialization<O> - Interface in elki.clustering.kmedoids.initialization

Interface for initializing K-Medoids.

KMedoidsKMedoidsInitialization<O> - Class in elki.clustering.kmedoids.initialization

Initialize k-medoids with k-medoids, for methods such as PAMSIL.
This could also be used to initialize, e.g., PAM with CLARA.

KMedoidsKMedoidsInitialization(KMedoidsClustering<O>) - Constructor for class elki.clustering.kmedoids.initialization.KMedoidsKMedoidsInitialization

Constructor.

KMedoidsKMedoidsInitialization.Par<O> - Class in elki.clustering.kmedoids.initialization

Parameterization class.

KMPP_DISTANCE_ID - Static variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves.Par

k Means distance.

KMPP_DISTANCE_ID - Static variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree.Par

k Means distance.

KNNKernelDensityMinimaClustering - Class in elki.clustering.onedimensional

Cluster one-dimensional data by splitting the data set on local minima after performing kernel density estimation.

KNNKernelDensityMinimaClustering(int, KernelDensityFunction, KNNKernelDensityMinimaClustering.Mode, int, int) - Constructor for class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering

Constructor.

KNNKernelDensityMinimaClustering.Mode - Enum in elki.clustering.onedimensional

Estimation mode.

KNNKernelDensityMinimaClustering.Par - Class in elki.clustering.onedimensional

Parameterization class.

kplus - Variable in class elki.clustering.dbscan.LSDBC

Number of neighbors (+ query point)

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