| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms
Clustering algorithms are supposed to implement the
Algorithm-Interface. |
| de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation |
Affinity Propagation (AP) clustering.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN
Generalized DBSCAN is an abstraction of the original DBSCAN idea,
that allows the use of arbitrary "neighborhood" and "core point" predicates.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased |
Angle-based outlier detection algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.subspace |
Subspace outlier detection methods
Methods that detect outliers in subspaces (projections) of the data set.
|
| de.lmu.ifi.dbs.elki.database |
ELKI database layer - loading, storing, indexing and accessing data
|
| de.lmu.ifi.dbs.elki.database.query.distance |
Prepared queries for distances
|
| de.lmu.ifi.dbs.elki.database.query.range |
Prepared queries for ε-range queries, that return all objects within
the radius ε
|
| de.lmu.ifi.dbs.elki.database.query.similarity |
Prepared queries for similarity functions
|
| de.lmu.ifi.dbs.elki.database.relation |
Relations, materialized and virtual (views)
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.adapter |
Distance functions deriving distances from, e.g., similarity measures
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic |
Distance from probability theory, mostly divergences such as K-L-divergence,
J-divergence, F-divergence, χ²-divergence, etc.
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.set |
Distance functions for binary and set type data.
|
| de.lmu.ifi.dbs.elki.distance.similarityfunction |
Similarity functions
|
| de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster |
Similarity measures for comparing clusters.
|
| de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
Kernel functions.
|
| de.lmu.ifi.dbs.elki.index |
Index structure implementations
|
| de.lmu.ifi.dbs.elki.index.distancematrix |
Precomputed distance matrix.
|
| Class and Description |
|---|
| SharedNearestNeighborSimilarityFunction
SharedNearestNeighborSimilarityFunction with a pattern defined to accept
Strings that define a non-negative Integer.
|
| Class and Description |
|---|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| PrimitiveSimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| PrimitiveSimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| PrimitiveSimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| NormalizedSimilarityFunction
Marker interface to signal that the similarity function is normalized to
produce values in the range of [0:1].
|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| NormalizedPrimitiveSimilarityFunction
Marker interface for similarity functions working on primitive objects, and
limited to the 0-1 value range.
|
| NormalizedSimilarityFunction
Marker interface to signal that the similarity function is normalized to
produce values in the range of [0:1].
|
| PrimitiveSimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| NormalizedPrimitiveSimilarityFunction
Marker interface for similarity functions working on primitive objects, and
limited to the 0-1 value range.
|
| NormalizedSimilarityFunction
Marker interface to signal that the similarity function is normalized to
produce values in the range of [0:1].
|
| PrimitiveSimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| AbstractIndexBasedSimilarityFunction
Abstract super class for distance functions needing a preprocessor.
|
| AbstractIndexBasedSimilarityFunction.Instance
The actual instance bound to a particular database.
|
| AbstractIndexBasedSimilarityFunction.Parameterizer
Parameterization class.
|
| AbstractVectorSimilarityFunction
Abstract base class for double-valued primitive similarity functions.
|
| DBIDSimilarityFunction
Interface DBIDSimilarityFunction describes the requirements of any similarity
function defined over object IDs.
|
| FractionalSharedNearestNeighborSimilarityFunction
SharedNearestNeighborSimilarityFunction with a pattern defined to accept
Strings that define a non-negative Integer.
|
| FractionalSharedNearestNeighborSimilarityFunction.Instance
Actual instance for a dataset.
|
| IndexBasedSimilarityFunction
Interface for preprocessor/index based similarity functions.
|
| IndexBasedSimilarityFunction.Instance
Instance interface for index/preprocessor based distance functions.
|
| Kulczynski1SimilarityFunction
Kulczynski similarity 1.
|
| Kulczynski2SimilarityFunction
Kulczynski similarity 2.
|
| NormalizedSimilarityFunction
Marker interface to signal that the similarity function is normalized to
produce values in the range of [0:1].
|
| PrimitiveSimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| SharedNearestNeighborSimilarityFunction
SharedNearestNeighborSimilarityFunction with a pattern defined to accept
Strings that define a non-negative Integer.
|
| SharedNearestNeighborSimilarityFunction.Instance
Instance for a particular database.
|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| NormalizedSimilarityFunction
Marker interface to signal that the similarity function is normalized to
produce values in the range of [0:1].
|
| PrimitiveSimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| AbstractVectorSimilarityFunction
Abstract base class for double-valued primitive similarity functions.
|
| PrimitiveSimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
| Class and Description |
|---|
| SimilarityFunction
Interface SimilarityFunction describes the requirements of any similarity
function.
|
Copyright © 2019 ELKI Development Team. License information.