| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical |
Hierarchical agglomerative clustering (HAC).
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization |
Initialization strategies for k-means.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.distance |
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms.
|
| de.lmu.ifi.dbs.elki.database.datastore |
General data store layer API (along the lines of
Map<DBID, T> - use everywhere!) |
| de.lmu.ifi.dbs.elki.database.datastore.memory |
Memory data store implementation for ELKI.
|
| de.lmu.ifi.dbs.elki.index.invertedlist |
Indexes using inverted lists.
|
| de.lmu.ifi.dbs.elki.parallel.processor |
Processor API of ELKI, and some essential shared processors.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
LSDBC.fillDensities(KNNQuery<O> knnq,
DBIDs ids,
WritableDoubleDataStore dens)
Collect all densities into an array for sorting.
|
private boolean |
LSDBC.isLocalMaximum(double kdist,
DBIDs neighbors,
WritableDoubleDataStore kdists)
Test if a point is a local density maximum.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
SLINK.step1(DBIDRef id,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda)
First step: Initialize P(id) = id, L(id) = infinity.
|
private void |
SLINK.step2(DBIDRef id,
DBIDs processedIDs,
DistanceQuery<? super O> distQuery,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINK.step2primitive(DBIDRef id,
DBIDs processedIDs,
Relation<? extends O> relation,
PrimitiveDistanceFunction<? super O> distFunc,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINK.step3(DBIDRef id,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
DBIDs processedIDs,
WritableDoubleDataStore m)
Third step: Determine the values for P and L
|
private void |
SLINK.step4(DBIDRef id,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
DBIDs processedIDs)
Fourth step: Actualize the clusters if necessary
|
| Modifier and Type | Method and Description |
|---|---|
protected boolean |
KMedoidsPAM.assignToNearestCluster(ArrayDBIDs means,
DBIDs ids,
WritableDoubleDataStore second,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
| Modifier and Type | Method and Description |
|---|---|
protected double |
KMeansPlusPlusInitialMeans.initialWeights(WritableDoubleDataStore weights,
DBIDs ids,
DBIDRef latest,
DistanceQuery<?> distQ)
Initialize the weight list.
|
protected <T> double |
KMeansPlusPlusInitialMeans.updateWeights(WritableDoubleDataStore weights,
DBIDs ids,
T latest,
DistanceQuery<? super T> distQ)
Update the weight list.
|
| 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 void |
DWOF.initializeRadii(DBIDs ids,
KNNQuery<O> knnq,
DistanceQuery<O> distFunc,
WritableDoubleDataStore radii)
This method prepares a container for the radii of the objects and
initializes radii according to the equation:
initialRadii of a certain object = (absoluteMinDist of all objects) *
(avgDist of the object) / (minAvgDist of all objects)
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
ReferenceBasedOutlierDetection.updateDensities(WritableDoubleDataStore rbod_score,
DoubleDBIDList referenceDists)
Update the density estimates for each object.
|
| Modifier and Type | Field and Description |
|---|---|
private WritableDoubleDataStore |
FlexibleLOF.LOFResult.lofs
The LOF values of the objects.
|
private WritableDoubleDataStore |
FlexibleLOF.LOFResult.lrds
The LRD values of the objects.
|
| Modifier and Type | Method and Description |
|---|---|
WritableDoubleDataStore |
FlexibleLOF.LOFResult.getLofs()
Get the LOF data store.
|
WritableDoubleDataStore |
FlexibleLOF.LOFResult.getLrds()
Get the LRD data store.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
COF.computeAverageChainingDistances(KNNQuery<O> knnq,
DistanceQuery<O> dq,
DBIDs ids,
WritableDoubleDataStore acds)
Computes the average chaining distance, the average length of a path
through the given set of points to each target.
|
private void |
COF.computeCOFScores(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore acds,
WritableDoubleDataStore cofs,
DoubleMinMax cofminmax)
Compute Connectivity outlier factors.
|
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 |
FlexibleLOF.computeLOFs(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore lrds,
WritableDoubleDataStore lofs,
DoubleMinMax lofminmax)
Computes the Local outlier factor (LOF) of the specified objects.
|
private void |
LOF.computeLOFScores(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore lrds,
WritableDoubleDataStore lofs,
DoubleMinMax lofminmax)
Compute local outlier factors.
|
protected void |
FlexibleLOF.computeLRDs(KNNQuery<O> knnq,
DBIDs ids,
WritableDoubleDataStore lrds)
Computes the local reachability density (LRD) of the specified objects.
|
private void |
LOF.computeLRDs(KNNQuery<O> knnq,
DBIDs ids,
WritableDoubleDataStore lrds)
Compute local reachability distances.
|
protected void |
INFLO.computeNeighborhoods(Relation<O> relation,
KNNQuery<O> knnQuery,
ModifiableDBIDs pruned,
WritableDataStore<ModifiableDBIDs> knns,
WritableDataStore<ModifiableDBIDs> rnns,
WritableDoubleDataStore density)
Compute neighborhoods
|
protected void |
KDEOS.computeOutlierScores(KNNQuery<O> knnq,
DBIDs ids,
WritableDataStore<double[]> densities,
WritableDoubleDataStore kdeos,
DoubleMinMax minmax)
Compute the final KDEOS scores.
|
protected void |
LoOP.computePDists(Relation<O> relation,
KNNQuery<O> knn,
WritableDoubleDataStore pdists)
Compute the probabilistic distances used by LoOP.
|
protected double |
LoOP.computePLOFs(Relation<O> relation,
KNNQuery<O> knn,
WritableDoubleDataStore pdists,
WritableDoubleDataStore plofs)
Compute the LOF values, using the pdist distances.
|
private void |
SimplifiedLOF.computeSimplifiedLOFs(DBIDs ids,
KNNQuery<O> knnq,
WritableDoubleDataStore slrds,
WritableDoubleDataStore lofs,
DoubleMinMax lofminmax)
Compute the simplified LOF factors.
|
private void |
SimplifiedLOF.computeSimplifiedLRDs(DBIDs ids,
KNNQuery<O> knnq,
WritableDoubleDataStore lrds)
Compute the simplified reachability densities.
|
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
|
| Constructor and Description |
|---|
LOFResult(OutlierResult result,
KNNQuery<O> kNNRefer,
KNNQuery<O> kNNReach,
WritableDoubleDataStore lrds,
WritableDoubleDataStore lofs)
Encapsulates information generated during a run of the
FlexibleLOF algorithm. |
| Modifier and Type | Method and Description |
|---|---|
static WritableDoubleDataStore |
DataStoreUtil.makeDoubleStorage(DBIDs ids,
int hints)
Make a new storage, to associate the given ids with an object of class
dataclass.
|
WritableDoubleDataStore |
DataStoreFactory.makeDoubleStorage(DBIDs ids,
int hints)
Make a new storage, to associate the given ids with an object of class
dataclass.
|
static WritableDoubleDataStore |
DataStoreUtil.makeDoubleStorage(DBIDs ids,
int hints,
double def)
Make a new storage, to associate the given ids with an object of class
dataclass.
|
WritableDoubleDataStore |
DataStoreFactory.makeDoubleStorage(DBIDs ids,
int hints,
double def)
Make a new storage, to associate the given ids with an object of class
dataclass.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ArrayDoubleStore
A class to answer representation queries using the stored Array.
|
class |
MapIntegerDBIDDoubleStore
Writable data store for double values.
|
| Modifier and Type | Method and Description |
|---|---|
WritableDoubleDataStore |
MemoryDataStoreFactory.makeDoubleStorage(DBIDs ids,
int hints) |
WritableDoubleDataStore |
MemoryDataStoreFactory.makeDoubleStorage(DBIDs ids,
int hints,
double def) |
| Modifier and Type | Field and Description |
|---|---|
(package private) WritableDoubleDataStore |
InMemoryInvertedIndex.length
Length storage.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
InMemoryInvertedIndex.naiveQuery(V obj,
WritableDoubleDataStore scores,
HashSetModifiableDBIDs cands)
Query the most similar objects, abstract version.
|
private double |
InMemoryInvertedIndex.naiveQueryDense(NumberVector obj,
WritableDoubleDataStore scores,
HashSetModifiableDBIDs cands)
Query the most similar objects, dense version.
|
private double |
InMemoryInvertedIndex.naiveQuerySparse(SparseNumberVector obj,
WritableDoubleDataStore scores,
HashSetModifiableDBIDs cands)
Query the most similar objects, sparse version.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) WritableDoubleDataStore |
WriteDoubleDataStoreProcessor.store
Store to write to
|
| Constructor and Description |
|---|
WriteDoubleDataStoreProcessor(WritableDoubleDataStore store)
Constructor.
|
Copyright © 2014 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.