| Package | Description | 
|---|---|
| de.lmu.ifi.dbs.elki.algorithm | Algorithms suitable as a task for the  KDDTaskmain 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
 
 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.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. | 
| tutorial.clustering | Classes from the tutorial on implementing a custom k-means variation. | 
| 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  MeanModelthat 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  MeanModelthat 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  MeanModelthat 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. | 
| Class and Description | 
|---|
| MeanModel Cluster model that stores a mean for the cluster. | 
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.