Uses of Interface
de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs

Packages that use ModifiableDBIDs

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash	Helper classes for the CASH algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique	Helper classes for the CLIQUE algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms The algorithms in this package perform statistical analysis of the data (e.g. compute distributions, distance distributions etc.)
de.lmu.ifi.dbs.elki.database.ids	Database object identification and ID group handling API.
de.lmu.ifi.dbs.elki.database.ids.integer	Integer-based DBID implementation -- do not use directly - always use DBIDUtil.
de.lmu.ifi.dbs.elki.index.preprocessed.preference	Indexes storing preference vectors.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp	MkAppTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop	MkCoPTree
de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments	Visualizers for inspecting cluster differences using pair counting segments.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering declared as ModifiableDBIDs

Modifier and Type	Field and Description
protected ModifiableDBIDs	SNNClustering.noise Holds a set of noise.
protected ModifiableDBIDs	DBSCAN.noise Holds a set of noise.
protected ModifiableDBIDs	SNNClustering.processedIDs Holds a set of processed ids.
protected ModifiableDBIDs	DBSCAN.processedIDs Holds a set of processed ids.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type ModifiableDBIDs

Modifier and Type	Field and Description
protected List<ModifiableDBIDs>	SNNClustering.resultList Holds a list of clusters found.
protected List<ModifiableDBIDs>	DBSCAN.resultList Holds a list of clusters found.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
private void	DBSCAN.processNeighbors(DBIDIter neighbor, ModifiableDBIDs currentCluster, HashSetModifiableDBIDs seeds) Process a single core point.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as ModifiableDBIDs

Modifier and Type	Field and Description
(package private) ModifiableDBIDs	ORCLUS.ORCLUSCluster.objectIDs The ids of the objects belonging to this cluster.
private ModifiableDBIDs	CASH.processedIDs Holds a set of processed ids.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
private Matrix	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dim, CASHInterval interval, ModifiableDBIDs ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash declared as ModifiableDBIDs

Modifier and Type	Field and Description
private ModifiableDBIDs	CASHInterval.ids Holds the ids of the objects associated with this interval.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash that return ModifiableDBIDs

Modifier and Type	Method and Description
ModifiableDBIDs	CASHIntervalSplit.determineIDs(DBIDs superSetIDs, HyperBoundingBox interval, double d_min, double d_max) Determines the ids belonging to the given interval, i.e. the parameterization functions falling within the interval.
ModifiableDBIDs	CASHInterval.getIDs() Returns the set of ids of the objects associated with this interval.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash with parameters of type ModifiableDBIDs

Constructor and Description
CASHInterval(double[] min, double[] max, CASHIntervalSplit split, ModifiableDBIDs ids, int maxSplitDimension, int level, double d_min, double d_max) Provides a unique interval represented by its id, a hyper bounding box and a set of objects ids associated with this interval.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction declared as ModifiableDBIDs

Modifier and Type	Field and Description
protected ModifiableDBIDs	HDBSCANHierarchyExtraction.TempCluster.members New ids, not yet in child clusters.
protected ModifiableDBIDs	SimplifiedHierarchyExtraction.TempCluster.newids New ids, not yet in child clusters.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with type arguments of type ModifiableDBIDs

Modifier and Type	Method and Description
protected boolean	KMedoidsEM.assignToNearestCluster(ArrayDBIDs means, Mean[] mdist, List<? extends ModifiableDBIDs> clusters, DistanceQuery<V> distQ) Returns a list of clusters.
protected boolean	AbstractKMeans.assignToNearestCluster(Relation<? extends V> relation, List<? extends NumberVector> means, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum) Returns a list of clusters.
protected boolean	KMeansBatchedLloyd.assignToNearestCluster(Relation<V> relation, DBIDs ids, List<? extends NumberVector> oldmeans, double[][] meanshift, int[] changesize, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum) Returns a list of clusters.
private int	KMeansElkan.assignToNearestCluster(Relation<V> relation, List<Vector> means, List<Vector> sums, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] sep, double[][] cdist, WritableDoubleDataStore upper, WritableDataStore<double[]> lower) Reassign objects, but only if their bounds indicate it is necessary to do so.
private int	KMeansHamerly.assignToNearestCluster(Relation<V> relation, List<Vector> means, List<Vector> sums, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] sep, WritableDoubleDataStore upper, WritableDoubleDataStore lower) Reassign objects, but only if their bounds indicate it is necessary to do so.
private int	KMeansElkan.initialAssignToNearestCluster(Relation<V> relation, List<Vector> means, List<Vector> sums, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, WritableDoubleDataStore upper, WritableDataStore<double[]> lower) Reassign objects, but only if their bounds indicate it is necessary to do so.
private int	KMeansHamerly.initialAssignToNearestCluster(Relation<V> relation, List<Vector> means, List<Vector> sums, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, WritableDoubleDataStore upper, WritableDoubleDataStore lower) Reassign objects, but only if their bounds indicate it is necessary to do so.
protected boolean	AbstractKMeans.macQueenIterate(Relation<V> relation, List<Vector> means, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum) Perform a MacQueen style iteration.
protected List<Vector>	AbstractKMeans.means(List<? extends ModifiableDBIDs> clusters, List<? extends NumberVector> means, Relation<V> database) Returns the mean vectors of the given clusters in the given database.
protected List<Vector>	AbstractKMeans.medians(List<? extends ModifiableDBIDs> clusters, List<Vector> medians, Relation<V> database) Returns the median vectors of the given clusters in the given database.
protected boolean	AbstractKMeans.updateAssignment(DBIDIter iditer, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, int newA)
protected boolean	KMeansBatchedLloyd.updateAssignment(DBIDIter id, V fv, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[][] meanshift, int[] changesize, int minIndex) Update the assignment of a single object.
private boolean	AbstractKMeans.updateMeanAndAssignment(List<ModifiableDBIDs> clusters, List<Vector> means, int minIndex, V fv, DBIDIter iditer, WritableIntegerDataStore assignment) Try to update the cluster assignment.
protected void	KMeansBatchedLloyd.updateMeans(List<Vector> means, double[][] meanshift, List<ModifiableDBIDs> clusters, int[] changesize) Merge changes into mean vectors.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.optics declared as ModifiableDBIDs

Modifier and Type	Field and Description
(package private) ModifiableDBIDs	FastOPTICS.processed processed points
(package private) ModifiableDBIDs	OPTICSList.Instance.processedIDs Holds a set of processed ids.
protected ModifiableDBIDs	GeneralizedOPTICS.Instance.processedIDs Holds a set of processed ids.
private ModifiableDBIDs	OPTICSHeap.Instance.processedIDs Holds a set of processed ids.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace declared as ModifiableDBIDs

Modifier and Type	Field and Description
ModifiableDBIDs	P3C.ClusterCandidate.ids Objects contained in cluster.
(package private) ModifiableDBIDs	PROCLUS.PROCLUSCluster.objectIDs The ids of the objects belonging to this cluster.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that return types with arguments of type ModifiableDBIDs

Modifier and Type	Method and Description
private List<Pair<Subspace,ModifiableDBIDs>>	CLIQUE.determineClusters(List<CLIQUESubspace<V>> denseSubspaces) Determines the clusters in the specified dense subspaces.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
private void	P3C.assignUnassigned(Relation<V> relation, WritableDataStore<double[]> probClusterIGivenX, List<MultivariateGaussianModel> models, ModifiableDBIDs unassigned) Assign unassigned objects to best candidate based on shortest Mahalanobis distance.
private void	P3C.computeFuzzyMembership(Relation<V> relation, ArrayList<P3C.Signature> clusterCores, ModifiableDBIDs unassigned, WritableDataStore<double[]> probClusterIGivenX, List<MultivariateGaussianModel> models, int dim) Computes a fuzzy membership with the weights based on which cluster cores each data point is part of.
private void	P3C.findOutliers(Relation<V> relation, List<MultivariateGaussianModel> models, ArrayList<P3C.ClusterCandidate> clusterCandidates, ModifiableDBIDs noise) Performs outlier detection by testing the Mahalanobis distance of each point in a cluster against the critical value of the ChiSquared distribution with as many degrees of freedom as the cluster has relevant attributes.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type ModifiableDBIDs

Constructor and Description
PROCLUSCluster(ModifiableDBIDs objectIDs, long[] dimensions, Vector centroid) Constructor.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique declared as ModifiableDBIDs

Modifier and Type	Field and Description
private ModifiableDBIDs	CLIQUEUnit.ids The ids of the feature vectors this unit contains.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique that return types with arguments of type ModifiableDBIDs

Modifier and Type	Method and Description
List<Pair<Subspace,ModifiableDBIDs>>	CLIQUESubspace.determineClusters() Determines all clusters in this subspace by performing a depth-first search algorithm to find connected dense units.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
void	CLIQUESubspace.dfs(CLIQUEUnit<V> unit, ModifiableDBIDs cluster, CLIQUESubspace<V> model) Depth-first search algorithm to find connected dense units in this subspace that build a cluster.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique with parameters of type ModifiableDBIDs

Constructor and Description
CLIQUEUnit(ArrayList<CLIQUEInterval> intervals, ModifiableDBIDs ids) Creates a new k-dimensional unit for the given intervals.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain with type arguments of type ModifiableDBIDs

Modifier and Type	Method and Description
protected boolean	UKMeans.assignToNearestCluster(Relation<DiscreteUncertainObject> relation, List<Vector> means, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum) Returns a list of clusters.
protected List<Vector>	UKMeans.means(List<? extends ModifiableDBIDs> clusters, List<? extends NumberVector> means, Relation<DiscreteUncertainObject> database) Returns the mean vectors of the given clusters in the given database.
protected boolean	UKMeans.updateAssignment(DBIDIter iditer, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, int newA) Update the cluster assignment.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.outlier

Method parameters in de.lmu.ifi.dbs.elki.algorithm.outlier with type arguments of type ModifiableDBIDs

Modifier and Type	Method and Description
private void	DWOF.clusterData(DBIDs ids, RangeQuery<O> rnnQuery, WritableDoubleDataStore radii, WritableDataStore<ModifiableDBIDs> labels) This method applies a density based clustering algorithm.
private int	DWOF.updateSizes(DBIDs ids, WritableDataStore<ModifiableDBIDs> labels, WritableIntegerDataStore newSizes) This method updates each object's cluster size after the clustering step.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
protected void	INFLO.computeINFLO(Relation<O> relation, ModifiableDBIDs pruned, WritableDataStore<ModifiableDBIDs> knns, WritableDataStore<ModifiableDBIDs> rnns, WritableDoubleDataStore density, WritableDoubleDataStore inflos, DoubleMinMax inflominmax) Compute the final INFLO scores.
protected void	INFLO.computeNeighborhoods(Relation<O> relation, KNNQuery<O> knnQuery, ModifiableDBIDs pruned, WritableDataStore<ModifiableDBIDs> knns, WritableDataStore<ModifiableDBIDs> rnns, WritableDoubleDataStore density) Compute neighborhoods

Method parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with type arguments of type ModifiableDBIDs

Modifier and Type	Method and Description
protected void	INFLO.computeINFLO(Relation<O> relation, ModifiableDBIDs pruned, WritableDataStore<ModifiableDBIDs> knns, WritableDataStore<ModifiableDBIDs> rnns, WritableDoubleDataStore density, WritableDoubleDataStore inflos, DoubleMinMax inflominmax) Compute the final INFLO scores.
protected void	INFLO.computeINFLO(Relation<O> relation, ModifiableDBIDs pruned, WritableDataStore<ModifiableDBIDs> knns, WritableDataStore<ModifiableDBIDs> rnns, WritableDoubleDataStore density, WritableDoubleDataStore inflos, DoubleMinMax inflominmax) Compute the final INFLO scores.
protected void	INFLO.computeNeighborhoods(Relation<O> relation, KNNQuery<O> knnQuery, ModifiableDBIDs pruned, WritableDataStore<ModifiableDBIDs> knns, WritableDataStore<ModifiableDBIDs> rnns, WritableDoubleDataStore density) Compute neighborhoods
protected void	INFLO.computeNeighborhoods(Relation<O> relation, KNNQuery<O> knnQuery, ModifiableDBIDs pruned, WritableDataStore<ModifiableDBIDs> knns, WritableDataStore<ModifiableDBIDs> rnns, WritableDoubleDataStore density) Compute neighborhoods
protected DBIDs	INFLO.getKNN(DBIDIter q, KNNQuery<O> knnQuery, WritableDataStore<ModifiableDBIDs> knns, WritableDoubleDataStore density) Get the (forward only) kNN of an object, including the query point

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.algorithm.statistics

Methods in de.lmu.ifi.dbs.elki.algorithm.statistics with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
private void	EvaluateRetrievalPerformance.findMatches(ModifiableDBIDs posn, Relation<?> lrelation, Object label) Find all matching objects.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.database.ids

Subinterfaces of ModifiableDBIDs in de.lmu.ifi.dbs.elki.database.ids

Modifier and Type	Interface and Description
interface	ArrayModifiableDBIDs Array-oriented implementation of a modifiable DBID collection.
interface	HashSetModifiableDBIDs Set-oriented implementation of a modifiable DBID collection.

Methods in de.lmu.ifi.dbs.elki.database.ids that return ModifiableDBIDs

Modifier and Type	Method and Description
static ModifiableDBIDs	DBIDUtil.difference(DBIDs ids1, DBIDs ids2) Returns the difference of the two specified collection of IDs.
static ModifiableDBIDs	DBIDUtil.ensureModifiable(DBIDs ids) Ensure modifiable.
static ModifiableDBIDs	DBIDUtil.intersection(DBIDs first, DBIDs second) Compute the set intersection of two sets.
static ModifiableDBIDs	DBIDUtil.randomSample(DBIDs source, int k, int seed) Produce a random sample of the given DBIDs.
static ModifiableDBIDs	DBIDUtil.randomSample(DBIDs source, int k, Long seed) Produce a random sample of the given DBIDs.
static ModifiableDBIDs	DBIDUtil.randomSample(DBIDs source, int k, Random random) Produce a random sample of the given DBIDs.
static ModifiableDBIDs	DBIDUtil.randomSample(DBIDs source, int k, RandomFactory rnd) Produce a random sample of the given DBIDs.
static ModifiableDBIDs	DBIDUtil.union(DBIDs ids1, DBIDs ids2) Returns the union of the two specified collection of IDs.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.database.ids.integer

Classes in de.lmu.ifi.dbs.elki.database.ids.integer that implement ModifiableDBIDs

Modifier and Type	Class and Description
(package private) class	ArrayModifiableIntegerDBIDs Class using a primitive int[] array as storage.
(package private) class	TroveHashSetModifiableDBIDs Implementation using GNU Trove Int Hash Sets.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.index.preprocessed.preference

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.preference with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
private long[]	DiSHPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation, ModifiableDBIDs[] neighborIDs, StringBuilder msg) Determines the preference vector according to the specified neighbor ids.
private long[]	DiSHPreferenceVectorIndex.determinePreferenceVectorByApriori(Relation<V> relation, ModifiableDBIDs[] neighborIDs, StringBuilder msg) Determines the preference vector with the apriori strategy.
private long[]	DiSHPreferenceVectorIndex.determinePreferenceVectorByMaxIntersection(ModifiableDBIDs[] neighborIDs, StringBuilder msg) Determines the preference vector with the max intersection strategy.
private int	DiSHPreferenceVectorIndex.maxIntersection(Map<Integer,ModifiableDBIDs> candidates, DBIDs set, ModifiableDBIDs result) Returns the index of the set having the maximum intersection set with the specified set contained in the specified map.

Method parameters in de.lmu.ifi.dbs.elki.index.preprocessed.preference with type arguments of type ModifiableDBIDs

Modifier and Type	Method and Description
private int	DiSHPreferenceVectorIndex.max(Map<Integer,ModifiableDBIDs> candidates) Returns the set with the maximum size contained in the specified map.
private int	DiSHPreferenceVectorIndex.maxIntersection(Map<Integer,ModifiableDBIDs> candidates, DBIDs set, ModifiableDBIDs result) Returns the index of the set having the maximum intersection set with the specified set contained in the specified map.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
private void	MkAppTree.leafEntryIDs(MkAppTreeNode<O> node, ModifiableDBIDs result) Determines the ids of the leaf entries stored in the specified subtree.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
private void	MkCoPTree.doReverseKNNQuery(int k, DBIDRef q, ModifiableDoubleDBIDList result, ModifiableDBIDs candidates) Performs a reverse knn query.

Uses of ModifiableDBIDs in de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments declared as ModifiableDBIDs

Modifier and Type	Field and Description
protected ModifiableDBIDs	SegmentsStylingPolicy.unselectedObjects Not selected IDs that will be drawn in default colors.

Uses of ModifiableDBIDs in tutorial.clustering

Methods in tutorial.clustering with parameters of type ModifiableDBIDs

Modifier and Type	Method and Description
protected void	SameSizeKMeansAlgorithm.transfer(WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, SameSizeKMeansAlgorithm.Meta meta, ModifiableDBIDs src, ModifiableDBIDs dst, DBIDRef id, Integer dstnum) Transfer a single element from one cluster to another.

Method parameters in tutorial.clustering with type arguments of type ModifiableDBIDs

Modifier and Type	Method and Description
protected ArrayModifiableDBIDs	SameSizeKMeansAlgorithm.initialAssignment(List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, DBIDs ids)
protected List<Vector>	SameSizeKMeansAlgorithm.refineResult(Relation<V> relation, List<Vector> means, List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, ArrayModifiableDBIDs tids) Perform k-means style iterations to improve the clustering result.