Uses of Class
de.lmu.ifi.dbs.elki.math.Mean

Packages that use Mean

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.math	Mathematical operations and utilities used throughout the framework.
de.lmu.ifi.dbs.elki.math.dimensionsimilarity	Functions to compute the similarity of dimensions (or the interestingness of the combination).

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

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

Modifier and Type	Method and Description
protected boolean	KMedoidsEM.assignToNearestCluster(ArrayDBIDs means, Mean[] mdist, List<? extends ModifiableDBIDs> clusters, DistanceQuery<V,D> distQ) Returns a list of clusters.

Uses of Mean in de.lmu.ifi.dbs.elki.math

Subclasses of Mean in de.lmu.ifi.dbs.elki.math

Modifier and Type	Class and Description
class	MeanVariance Do some simple statistics (mean, variance) using a numerically stable online algorithm.
class	MeanVarianceMinMax Class collecting mean, variance, minimum and maximum statistics.
class	StatisticalMoments Track various statistical moments, including mean, variance, skewness and kurtosis.

Methods in de.lmu.ifi.dbs.elki.math that return Mean

Modifier and Type	Method and Description
static Mean[]	Mean.newArray(int dimensionality) Create and initialize a new array of MeanVariance

Methods in de.lmu.ifi.dbs.elki.math with parameters of type Mean

Modifier and Type	Method and Description
void	Mean.put(Mean other) Join the data of another MeanVariance instance.
void	MeanVariance.put(Mean other) Join the data of another MeanVariance instance.
void	StatisticalMoments.put(Mean other) Join the data of another MeanVariance instance.
void	MeanVarianceMinMax.put(Mean other)

Constructors in de.lmu.ifi.dbs.elki.math with parameters of type Mean

Constructor and Description
Mean(Mean other) Constructor from other instance

Uses of Mean in de.lmu.ifi.dbs.elki.math.dimensionsimilarity

Methods in de.lmu.ifi.dbs.elki.math.dimensionsimilarity with parameters of type Mean

Modifier and Type	Method and Description
private void	MCEDimensionSimilarity.divide(DBIDArrayIter it, double[] data, ArrayList<DBIDs> idx, int start, int end, int depth, Mean mean) Recursive call to further subdivide the array.