Package | Description |
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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.
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de.lmu.ifi.dbs.elki.algorithm.classification |
Classification algorithms.
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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.
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de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering |
Biclustering algorithms
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de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
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de.lmu.ifi.dbs.elki.algorithm.clustering.em |
Expectation-Maximization clustering algorithm.
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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.
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de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel |
Parallel versions of Generalized DBSCAN.
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de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical |
Hierarchical agglomerative clustering (HAC).
|
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction |
Extraction of partitional clusterings from hierarchical results.
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de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations
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de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization |
Initialization strategies for k-means.
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de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel |
Parallelized implementations of k-means.
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de.lmu.ifi.dbs.elki.algorithm.clustering.meta |
Meta clustering algorithms, that get their result from other clusterings or external sources.
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de.lmu.ifi.dbs.elki.algorithm.clustering.optics |
OPTICS family of clustering algorithms.
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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.itemsetmining |
Algorithms for frequent itemset mining such as APRIORI.
|
de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules |
Association rule mining.
|
de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
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de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased |
Angle-based outlier detection algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.distance |
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel |
Parallel implementations of distance-based outlier detectors.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic |
Outlier detection algorithms based on intrinsic dimensionality.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms
|
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel |
Parallelized variants of LOF.
|
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.spatial.neighborhood |
Spatial outlier neighborhood classes
|
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted |
Weighted Neighborhood definitions
|
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace |
Subspace outlier detection methods
Methods that detect outliers in subspaces (projections) of the data set.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial |
Trivial outlier detection algorithms: no outliers, all outliers, label
outliers.
|
de.lmu.ifi.dbs.elki.algorithm.projection |
Data projections (see also preprocessing filters for basic projections).
|
de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms.
|
de.lmu.ifi.dbs.elki.application.cache |
Utility applications for the persistence layer such as distance cache
builders.
|
de.lmu.ifi.dbs.elki.database |
ELKI database layer - loading, storing, indexing and accessing data
|
de.lmu.ifi.dbs.elki.evaluation |
Functionality for the evaluation of algorithms.
|
de.lmu.ifi.dbs.elki.evaluation.clustering |
Evaluation of clustering results
|
de.lmu.ifi.dbs.elki.evaluation.clustering.extractor |
Classes to extract clusterings from hierarchical clustering.
|
de.lmu.ifi.dbs.elki.evaluation.clustering.internal |
Internal evaluation measures for clusterings.
|
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.gui |
Package to provide a visualization GUI
|
de.lmu.ifi.dbs.elki.workflow |
Work flow packages, e.g., following the usual KDD model.
|
tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation
|
tutorial.outlier |
Tutorials on implementing outlier detection methods in ELKI.
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Database
Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database
Database specifies the requirements for any database implementation.
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Database
Database specifies the requirements for any database implementation.
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Class and Description |
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AbstractDatabase
Abstract base class for database API implementations.
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AbstractDatabase.Parameterizer
Parameterization class.
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Database
Database specifies the requirements for any database implementation.
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DatabaseEventManager
Class to manage database events such as insertions and removals.
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DatabaseEventManager.Type
Types for aggregation.
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HashmapDatabase
Database storing data using hashtable storage, and thus allowing additional
and removal of objects.
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StaticArrayDatabase
This database class uses array-based storage and thus does not allow for
dynamic insert, delete and update operations.
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UpdatableDatabase
Database API with updates.
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Class and Description |
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Database
Database specifies the requirements for any database implementation.
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Database
Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Database specifies the requirements for any database implementation.
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Class and Description |
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Database specifies the requirements for any database implementation.
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Class and Description |
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Database
Database specifies the requirements for any database implementation.
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Class and Description |
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Database
Database specifies the requirements for any database implementation.
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Class and Description |
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UpdatableDatabase
Database API with updates.
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Class and Description |
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Database
Database specifies the requirements for any database implementation.
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Class and Description |
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Database
Database specifies the requirements for any database implementation.
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Class and Description |
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Database specifies the requirements for any database implementation.
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Copyright © 2019 ELKI Development Team. License information.