Package | Description |
---|---|
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.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
de.lmu.ifi.dbs.elki.distance.distancevalue |
Distance values, i.e. object storing an actual distance value along with
comparison functions and value parsers.
|
Modifier and Type | Method and Description |
---|---|
private Clustering<SubspaceModel<V>> |
DiSH.computeClusters(Relation<V> database,
ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder,
DiSHDistanceFunction.Instance<V> distFunc)
Computes the hierarchical clusters according to the cluster order.
|
private Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> |
DiSH.extractClusters(Relation<V> database,
DiSHDistanceFunction.Instance<V> distFunc,
ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder)
Extracts the clusters from the cluster order.
|
Modifier and Type | Method and Description |
---|---|
PreferenceVectorBasedCorrelationDistance |
DiSHDistanceFunction.Instance.correlationDistance(V v1,
V v2,
BitSet pv1,
BitSet pv2)
Computes the correlation distance between the two specified vectors
according to the specified preference vectors.
|
abstract PreferenceVectorBasedCorrelationDistance |
AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance.correlationDistance(V v1,
V v2,
BitSet pv1,
BitSet pv2)
Computes the correlation distance between the two specified vectors
according to the specified preference vectors.
|
PreferenceVectorBasedCorrelationDistance |
HiSCDistanceFunction.Instance.correlationDistance(V v1,
V v2,
BitSet pv1,
BitSet pv2)
Computes the correlation distance between the two specified vectors
according to the specified preference vectors.
|
PreferenceVectorBasedCorrelationDistance |
AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance.distance(DBIDRef id1,
DBIDRef id2) |
PreferenceVectorBasedCorrelationDistance |
AbstractPreferenceVectorBasedCorrelationDistanceFunction.getDistanceFactory() |
Modifier and Type | Field and Description |
---|---|
static PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.FACTORY
The static factory instance
|
Modifier and Type | Method and Description |
---|---|
PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.infiniteDistance() |
PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.minus(PreferenceVectorBasedCorrelationDistance distance) |
PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.nullDistance() |
PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.parseString(String pattern) |
PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.plus(PreferenceVectorBasedCorrelationDistance distance) |
PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.undefinedDistance() |
Modifier and Type | Method and Description |
---|---|
int |
PreferenceVectorBasedCorrelationDistance.compareTo(PreferenceVectorBasedCorrelationDistance distance)
Checks if the dimensionality values of this distance and the specified
distance are equal.
|
PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.minus(PreferenceVectorBasedCorrelationDistance distance) |
PreferenceVectorBasedCorrelationDistance |
PreferenceVectorBasedCorrelationDistance.plus(PreferenceVectorBasedCorrelationDistance distance) |