| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms.
|
| 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.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.onedimensional |
Clustering algorithms for one-dimensional data.
|
| 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.evaluation.clustering |
Evaluation of clustering results.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.optics |
Visualizers that do work on OPTICS plots
|
| 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
DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected
sets in a database.
|
| DeLiClu
DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find
density-connected sets in a database.
|
| DeLiClu.SpatialObjectPair
Encapsulates an entry in the cluster order.
|
| EM
Provides the EM algorithm (clustering by expectation maximization).
|
| NaiveMeanShiftClustering
Mean-shift based clustering algorithm.
|
| OPTICS
OPTICS provides the OPTICS algorithm.
|
| OPTICSTypeAlgorithm
Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.
|
| OPTICSXi
Class to handle OPTICS Xi extraction.
|
| OPTICSXi.SteepArea
Data structure to represent a steep-down-area for the xi method.
|
| OPTICSXi.SteepDownArea
Data structure to represent a steep-down-area for the xi method.
|
| 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.
|
| AbstractProjectedDBSCAN
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
|
| AbstractProjectedDBSCAN.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. |
| OPTICS
OPTICS provides the OPTICS algorithm.
|
| OPTICSTypeAlgorithm
Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.
|
| 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.
|
| AbstractProjectedDBSCAN
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
|
| AbstractProjectedDBSCAN.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. |
| OPTICS
OPTICS provides the OPTICS algorithm.
|
| OPTICSTypeAlgorithm
Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.
|
| 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 |
|---|
| EM
Provides the EM algorithm (clustering by expectation maximization).
|
| 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 |
|---|
| OPTICSXi.SteepAreaResult
Result containing the chi-steep areas.
|
| 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. |