Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
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.em |
Expectation-Maximization clustering algorithm.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN.
|
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.initialization |
Initialization strategies for k-means.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel |
Parallelized implementations of k-means.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality |
Quality measures for k-Means results.
|
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.
|
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.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.data |
Basic classes for different data types, database object types and label types.
|
de.lmu.ifi.dbs.elki.data.model |
Cluster models classes for various algorithms.
|
de.lmu.ifi.dbs.elki.data.synthetic.bymodel |
Generator using a distribution model specified in an XML configuration file.
|
de.lmu.ifi.dbs.elki.data.type |
Data type information, also used for type restrictions.
|
de.lmu.ifi.dbs.elki.datasource.parser |
Parsers for different file formats and data types.
|
de.lmu.ifi.dbs.elki.evaluation.clustering |
Evaluation of clustering results.
|
de.lmu.ifi.dbs.elki.evaluation.outlier |
Evaluate an outlier score using a misclassification based cost model.
|
de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
de.lmu.ifi.dbs.elki.result.textwriter |
Text serialization (CSV, Gnuplot, Console, ...)
|
de.lmu.ifi.dbs.elki.visualization |
Visualization package of ELKI.
|
de.lmu.ifi.dbs.elki.visualization.opticsplot |
Code for drawing OPTICS plots
|
de.lmu.ifi.dbs.elki.visualization.visualizers.optics |
Visualizers that do work on OPTICS plots
|
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster |
Visualizers for clustering results based on 2D projections.
|
de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj |
Visualizers that do not use a particular projection.
|
Class and Description |
---|
CorrelationAnalysisSolution
A solution of correlation analysis is a matrix of equations describing the
dependencies.
|
Class and Description |
---|
MeanModel
Cluster model that stores a mean for the cluster.
|
Model
Base interface for Model classes.
|
PrototypeModel
Cluster model that stores a prototype for each cluster.
|
Class and Description |
---|
MedoidModel
Cluster model that stores a mean for the cluster.
|
Class and Description |
---|
BiclusterModel
Wrapper class to provide the basic properties of a Bicluster.
|
BiclusterWithInversionsModel
This code was factored out of the Bicluster class, since not all biclusters
have inverted rows.
|
Class and Description |
---|
CorrelationModel
Cluster model using a filtered PCA result and an centroid.
|
DimensionModel
Cluster model just providing a cluster dimensionality.
|
Model
Base interface for Model classes.
|
Class and Description |
---|
EMModel
Cluster model of an EM cluster, providing a mean and a full covariance
Matrix.
|
MeanModel
Cluster model that stores a mean for the cluster.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
DendrogramModel
Model for dendrograms, provides the distance to the child cluster.
|
Class and Description |
---|
KMeansModel
Trivial subclass of the
MeanModel that indicates the clustering to be
produced by k-means (so the Voronoi cell visualization is sensible). |
MeanModel
Cluster model that stores a mean for the cluster.
|
MedoidModel
Cluster model that stores a mean for the cluster.
|
Model
Base interface for Model classes.
|
Class and Description |
---|
MeanModel
Cluster model that stores a mean for the cluster.
|
Class and Description |
---|
KMeansModel
Trivial subclass of the
MeanModel that indicates the clustering to be
produced by k-means (so the Voronoi cell visualization is sensible). |
Class and Description |
---|
MeanModel
Cluster model that stores a mean for the cluster.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
ClusterModel
Generic cluster model.
|
Class and Description |
---|
OPTICSModel
Model for an OPTICS cluster
|
Class and Description |
---|
Model
Base interface for Model classes.
|
SubspaceModel
Model for Subspace Clusters.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
KMeansModel
Trivial subclass of the
MeanModel that indicates the clustering to be
produced by k-means (so the Voronoi cell visualization is sensible). |
Class and Description |
---|
SubspaceModel
Model for Subspace Clusters.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
AbstractModel
Abstract base class for Cluster Models.
|
BiclusterModel
Wrapper class to provide the basic properties of a Bicluster.
|
ClusterModel
Generic cluster model.
|
MeanModel
Cluster model that stores a mean for the cluster.
|
Model
Base interface for Model classes.
|
PrototypeModel
Cluster model that stores a prototype for each cluster.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
OPTICSModel
Model for an OPTICS cluster
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Class and Description |
---|
Model
Base interface for Model classes.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.