| 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.biclustering |
Biclustering algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.em |
Expectation-Maximization clustering algorithm.
|
| 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.clustering.gdbscan.parallel |
Parallel versions of Generalized DBSCAN.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch |
BIRCH clustering.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction |
Extraction of partitional clusterings from hierarchical results.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel |
Parallelized implementations of k-means.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.meta |
Meta clustering algorithms, that get their result from other clusterings or external sources.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional |
Clustering algorithms for one-dimensional data.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.optics |
OPTICS family of clustering algorithms.
|
| 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.clustering.uncertain |
Clustering algorithms for uncertain data.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
| de.lmu.ifi.dbs.elki.evaluation.clustering |
Evaluation of clustering results
|
| tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation
|
| Class and Description |
|---|
| CanopyPreClustering
Canopy pre-clustering is a simple preprocessing step for clustering.
|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| DBSCAN
Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to
find density-connected sets in a database.
|
| GriDBSCAN
Using Grid for Accelerating Density-Based Clustering.
|
| Leader
Leader clustering algorithm.
|
| NaiveMeanShiftClustering
Mean-shift based clustering algorithm.
|
| SNNClustering
Shared nearest neighbor clustering.
|
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| AbstractProjectedClustering |
| AbstractProjectedClustering.Parameterizer
Parameterization class.
|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| AbstractProjectedClustering |
| AbstractProjectedClustering.Parameterizer
Parameterization class.
|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Class and Description |
|---|
| ClusteringAlgorithm
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
Copyright © 2019 ELKI Development Team. License information.