Package | Description |
---|---|
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.
|
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.correlation.cash |
Helper classes for the
CASH algorithm. |
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.kmeans.quality |
Quality measures for k-Means results.
|
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.algorithm.outlier.lof |
LOF family of outlier detection algorithms.
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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
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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.
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de.lmu.ifi.dbs.elki.algorithm.outlier.trivial |
Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
|
de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms
The algorithms in this package perform statistical analysis of the data
(e.g. compute distributions, distance distributions etc.)
|
de.lmu.ifi.dbs.elki.application.greedyensemble |
Greedy ensembles for outlier detection.
|
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.database |
ELKI database layer - loading, storing, indexing and accessing data
|
de.lmu.ifi.dbs.elki.database.query |
Database queries - computing distances, neighbors, similarities - API and general documentation.
|
de.lmu.ifi.dbs.elki.database.query.distance |
Prepared queries for distances.
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de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
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de.lmu.ifi.dbs.elki.database.query.range |
Prepared queries for ε-range queries.
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de.lmu.ifi.dbs.elki.database.query.rknn |
Prepared queries for reverse k nearest neighbor (rkNN) queries.
|
de.lmu.ifi.dbs.elki.database.query.similarity |
Prepared queries for similarity functions.
|
de.lmu.ifi.dbs.elki.database.relation |
Relations, materialized and virtual (views).
|
de.lmu.ifi.dbs.elki.distance.distancefunction |
Distance functions for use within ELKI.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter |
Distance functions deriving distances from e.g. similarity measures
|
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation |
Distance functions using correlations.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
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de.lmu.ifi.dbs.elki.distance.similarityfunction |
Similarity functions.
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de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
Kernel functions.
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de.lmu.ifi.dbs.elki.evaluation.outlier |
Evaluate an outlier score using a misclassification based cost model.
|
de.lmu.ifi.dbs.elki.evaluation.roc |
Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
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de.lmu.ifi.dbs.elki.evaluation.similaritymatrix |
Render a distance matrix to visualize a clustering-distance-combination.
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de.lmu.ifi.dbs.elki.index |
Index structure implementations
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de.lmu.ifi.dbs.elki.index.lsh |
Locality Sensitive Hashing
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de.lmu.ifi.dbs.elki.index.lsh.hashfamilies |
Hash function families for LSH.
|
de.lmu.ifi.dbs.elki.index.preprocessed |
Index structure based on preprocessors
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de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
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de.lmu.ifi.dbs.elki.index.preprocessed.localpca |
Index using a preprocessed local PCA.
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de.lmu.ifi.dbs.elki.index.preprocessed.preference |
Indexes storing preference vectors.
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de.lmu.ifi.dbs.elki.index.preprocessed.snn |
Indexes providing nearest neighbor sets
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de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj |
Index using a preprocessed local subspaces.
|
de.lmu.ifi.dbs.elki.index.projected |
Projected indexes for data.
|
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees |
Metrical index structures based on the concepts of the M-Tree
supporting processing of reverse k nearest neighbor queries by
using the k-nn distances of the entries.
|
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree | |
de.lmu.ifi.dbs.elki.index.tree.spatial.kd |
K-d-tree and variants.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu | |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar | |
de.lmu.ifi.dbs.elki.index.vafile |
Vector Approximation File
|
de.lmu.ifi.dbs.elki.math.dimensionsimilarity |
Functions to compute the similarity of dimensions (or the interestingness of the combination).
|
de.lmu.ifi.dbs.elki.math.linearalgebra |
Linear Algebra package provides classes and computational methods for operations on matrices.
|
de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
|
de.lmu.ifi.dbs.elki.math.scales |
Scales handling for plotting.
|
de.lmu.ifi.dbs.elki.math.spacefillingcurves |
Space filling curves.
|
de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
de.lmu.ifi.dbs.elki.result.optics |
Result classes for OPTICS.
|
de.lmu.ifi.dbs.elki.result.outlier |
Outlier result classes
|
de.lmu.ifi.dbs.elki.result.textwriter |
Text serialization (CSV, Gnuplot, Console, ...)
|
de.lmu.ifi.dbs.elki.utilities |
Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
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de.lmu.ifi.dbs.elki.utilities.referencepoints |
Package containing strategies to obtain reference points
Shared code for various algorithms that use reference points.
|
de.lmu.ifi.dbs.elki.utilities.scaling.outlier |
Scaling of Outlier scores, that require a statistical analysis of the occurring values
|
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.visualization.projector |
Projectors are responsible for finding appropriate projections for data relations.
|
de.lmu.ifi.dbs.elki.visualization.visualizers.histogram |
Visualizers based on 1D projected histograms.
|
de.lmu.ifi.dbs.elki.visualization.visualizers.parallel |
Visualizers based on parallel coordinates.
|
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot |
Visualizers based on scatterplots.
|
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier |
Visualizers for outlier scores based on 2D projections.
|
tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation.
|
tutorial.outlier |
Class and Description |
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Relation
An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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MaterializedRelation
Represents a single representation.
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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DBIDView
Pseudo-representation that is the object ID itself.
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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ProxyView
A virtual partitioning of the database.
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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An object representation from a database
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