| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
| de.lmu.ifi.dbs.elki.algorithm.benchmark |
Benchmarking pseudo algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical | |
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
| 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.algorithm.clustering.trivial |
Trivial clustering algorithms: all in one, no clusters, label clusterings
These methods are mostly useful for providing a reference result in evaluation.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.meta |
Meta outlier detection algorithms: external scores, score rescaling.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.spatial |
Spatial outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.subspace |
Subspace outlier detection methods.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.trivial |
Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
|
| de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms
The algorithms in this package perform statistical analysis of the data
(e.g. compute distributions, distance distributions etc.)
|
| de.lmu.ifi.dbs.elki.workflow |
Work flow packages, e.g. following the usual KDD model, closely related to CRISP-DM
|
| tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation.
|
| tutorial.outlier |
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| AbstractPrimitiveDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractPrimitiveDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| APRIORI
Provides the APRIORI algorithm for Mining Association Rules.
|
| DependencyDerivator
Dependency derivator computes quantitatively linear dependencies among
attributes of a given dataset based on a linear correlation PCA.
|
| KNNDistanceOrder
Provides an order of the kNN-distances for all objects within the database.
|
| KNNJoin
Joins in a given spatial database to each object its k-nearest neighbors.
|
| KNNJoin.Task
Task in the processing queue.
|
| MaterializeDistances
Algorithm to materialize all the distances in a data set.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| KNNJoin
Joins in a given spatial database to each object its k-nearest neighbors.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractPrimitiveDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractPrimitiveDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| DependencyDerivator
Dependency derivator computes quantitatively linear dependencies among
attributes of a given dataset based on a linear correlation PCA.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| AbstractPrimitiveDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|
| Class and Description |
|---|
| AbstractAlgorithm
This class serves also as a model of implementing an algorithm within this
framework.
|
| AbstractDistanceBasedAlgorithm
Provides an abstract algorithm already setting the distance function.
|
| AbstractDistanceBasedAlgorithm.Parameterizer
Parameterization helper class.
|
| Algorithm
Specifies the requirements for any algorithm that is to be executable by the
main class.
|