Uses of Class
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.DimensionSimilarityMatrix

Packages that use DimensionSimilarityMatrix

Package	Description
de.lmu.ifi.dbs.elki.math.dimensionsimilarity	Functions to compute the similarity of dimensions (or the interestingness of the combination).

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

Subclasses of DimensionSimilarityMatrix in de.lmu.ifi.dbs.elki.math.dimensionsimilarity

Modifier and Type	Class and Description
static class	DimensionSimilarityMatrix.FullDimensionSimilarityMatrix Complete matrix of pairwise dimension similarities.
static class	DimensionSimilarityMatrix.PartialDimensionSimilarityMatrix Partial matrix of pairwise dimension similarities.

Methods in de.lmu.ifi.dbs.elki.math.dimensionsimilarity that return DimensionSimilarityMatrix

Modifier and Type	Method and Description
static DimensionSimilarityMatrix	DimensionSimilarityMatrix.make(int dims) Make a full dimension similarity matrix.
static DimensionSimilarityMatrix	DimensionSimilarityMatrix.make(int[] dims) Make a partial dimension similarity matrix.

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

Modifier and Type	Method and Description
private ArrayList<ArrayDBIDs>	HiCSDimensionSimilarity.buildOneDimIndexes(Relation<? extends NumberVector> relation, DBIDs ids, DimensionSimilarityMatrix matrix) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list
private ArrayList<ArrayList<DBIDs>>	MCEDimensionSimilarity.buildPartitions(Relation<? extends NumberVector> relation, DBIDs ids, int depth, DimensionSimilarityMatrix matrix) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list.
void	SlopeInversionDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	SlopeDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	SURFINGDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	MCEDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	HiCSDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	HSMDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	CovarianceDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	DimensionSimilarity.computeDimensionSimilarites(Relation<? extends V> relation, DBIDs subset, DimensionSimilarityMatrix matrix) Compute the dimension similarity matrix
double	DimensionSimilarityMatrix.PrimAdapter.distance(DimensionSimilarityMatrix data, int i, int j)
int	DimensionSimilarityMatrix.PrimAdapter.size(DimensionSimilarityMatrix data)