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Packages that use Algorithm | |
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de.lmu.ifi.dbs.elki | ELKI framework "Environment for Developing KDD-Applications Supported by Index-Structures"
KDDTask is the main class of the ELKI-Framework
for command-line interaction. |
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
Clustering algorithms are supposed to implement the Algorithm -Interface. |
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation | Correlation 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.outlier | Outlier detection algorithms |
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.evaluation.histogram | Functionality for the evaluation of algorithms using histograms. |
de.lmu.ifi.dbs.elki.evaluation.roc | Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve) |
Uses of Algorithm in de.lmu.ifi.dbs.elki |
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Fields in de.lmu.ifi.dbs.elki declared as Algorithm | |
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private Algorithm<O,Result> |
KDDTask.algorithm
Holds the algorithm to run. |
Fields in de.lmu.ifi.dbs.elki with type parameters of type Algorithm | |
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private ObjectParameter<Algorithm<O,Result>> |
KDDTask.ALGORITHM_PARAM
Parameter to specify the algorithm to be applied, must extend Algorithm . |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm |
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Classes in de.lmu.ifi.dbs.elki.algorithm that implement Algorithm | |
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class |
AbstractAlgorithm<O extends DatabaseObject,R extends Result>
AbstractAlgorithm sets the values for flags verbose and time. |
class |
APRIORI
Provides the APRIORI algorithm for Mining Association Rules. |
class |
DependencyDerivator<V extends NumberVector<V,?>,D extends Distance<D>>
Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA. |
class |
DistanceBasedAlgorithm<O extends DatabaseObject,D extends Distance<D>,R extends Result>
Provides an abstract algorithm already setting the distance function. |
class |
DummyAlgorithm<V extends NumberVector<V,?>>
Dummy Algorithm, which just iterates over all points once, doing a 10NN query each. |
class |
KNNDistanceOrder<O extends DatabaseObject,D extends Distance<D>>
Provides an order of the kNN-distances for all objects within the database. |
class |
KNNJoin<V extends NumberVector<V,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
Joins in a given spatial database to each object its k-nearest neighbors. |
class |
MaterializeDistances<V extends DatabaseObject,D extends NumberDistance<D,N>,N extends Number>
Algorithm to materialize all the distances in a data set. |
class |
MetaMultiAlgorithm<O extends DatabaseObject>
Meta algorithm that will run multiple algorithms and join the result. |
class |
NullAlgorithm<V extends NumberVector<V,?>>
Null Algorithm, which does nothing. |
Fields in de.lmu.ifi.dbs.elki.algorithm with type parameters of type Algorithm | |
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private List<Algorithm<O,Result>> |
MetaMultiAlgorithm.algorithms
The instantiated algorithms to run. |
private ObjectListParameter<Algorithm<O,Result>> |
MetaMultiAlgorithm.ALGORITHMS_PARAM
Parameter to specify the algorithm to be applied, must extend Algorithm . |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering |
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Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering | |
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interface |
ClusteringAlgorithm<C extends Clustering<? extends Model>,O extends DatabaseObject>
Interface for Algorithms that are capable to provide a Clustering as Result. |
Classes in de.lmu.ifi.dbs.elki.algorithm.clustering that implement Algorithm | |
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class |
ByLabelClustering<O extends DatabaseObject>
Pseudo clustering using labels. |
class |
ByLabelHierarchicalClustering<O extends DatabaseObject>
Pseudo clustering using labels. |
class |
DBSCAN<O extends DatabaseObject,D extends Distance<D>>
DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database. |
class |
DeLiClu<O extends NumberVector<O,?>,D extends Distance<D>>
DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database. |
class |
EM<V extends NumberVector<V,?>>
Provides the EM algorithm (clustering by expectation maximization). |
class |
KMeans<D extends Distance<D>,V extends NumberVector<V,?>>
Provides the k-means algorithm. |
class |
OPTICS<O extends DatabaseObject,D extends Distance<D>>
OPTICS provides the OPTICS algorithm. |
class |
ProjectedDBSCAN<V extends NumberVector<V,?>>
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor. |
class |
SLINK<O extends DatabaseObject,D extends Distance<D>>
Efficient implementation of the Single-Link Algorithm SLINK of R. |
class |
SNNClustering<O extends DatabaseObject,D extends Distance<D>>
Shared nearest neighbor clustering. |
class |
TrivialAllInOne<O extends DatabaseObject>
Trivial pseudo-clustering that just considers all points to be one big cluster. |
class |
TrivialAllNoise<O extends DatabaseObject>
Trivial pseudo-clustering that just considers all points to be noise. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that implement Algorithm | |
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class |
CASH
Provides the CASH algorithm, an subspace clustering algorithm based on the hough transform. |
class |
COPAC<V extends NumberVector<V,?>>
Provides the COPAC algorithm, an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions. |
class |
ERiC<V extends NumberVector<V,?>>
Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result. |
class |
FourC<O extends NumberVector<O,?>>
4C identifies local subgroups of data objects sharing a uniform correlation. |
class |
HiCO<V extends NumberVector<V,?>>
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters. |
class |
ORCLUS<V extends NumberVector<V,?>>
ORCLUS provides the ORCLUS algorithm, an algorithm to find clusters in high dimensional spaces. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that implement Algorithm | |
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class |
CLIQUE<V extends NumberVector<V,?>>
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality. |
class |
DiSH<V extends NumberVector<V,?>>
Algorithm for detecting subspace hierarchies. |
class |
HiSC<V extends NumberVector<V,?>>
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters. |
class |
PreDeCon<V extends NumberVector<V,?>>
PreDeCon computes clusters of subspace preference weighted connected points. |
class |
PROCLUS<V extends NumberVector<V,?>>
Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces. |
class |
ProjectedClustering<V extends NumberVector<V,?>>
Abstract superclass for projected clustering algorithms, like PROCLUS
and ORCLUS . |
class |
SUBCLU<V extends NumberVector<V,?>,D extends Distance<D>>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier |
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Classes in de.lmu.ifi.dbs.elki.algorithm.outlier that implement Algorithm | |
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class |
ABOD<V extends NumberVector<V,?>>
Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets. |
class |
AbstractDBOutlier<O extends DatabaseObject,D extends Distance<D>>
Simple distance based outlier detection algorithms. |
class |
DBOutlierDetection<O extends DatabaseObject,D extends Distance<D>>
Simple distanced based outlier detection algorithm. |
class |
DBOutlierScore<O extends DatabaseObject,D extends Distance<D>>
Compute percentage of neighbors in the given neighborhood with size d. |
class |
EMOutlier<V extends NumberVector<V,?>>
outlier detection algorithm using EM Clustering. |
class |
GaussianModel<V extends NumberVector<V,Double>>
Outlier have smallest GMOD_PROB: the outlier scores is the probability density of the assumed distribution. |
class |
GaussianUniformMixture<V extends NumberVector<V,Double>>
Outlier detection algorithm using a mixture model approach. |
class |
INFLO<O extends DatabaseObject>
INFLO provides the Mining Algorithms (Two-way Search Method) for Influence Outliers using Symmetric Relationship Reference: Jin, W., Tung, A., Han, J., and Wang, W. 2006 Ranking outliers using symmetric neighborhood relationship< br/> In Proc. |
class |
KNNOutlier<O extends DatabaseObject,D extends DoubleDistance>
Outlier Detection based on the distance of an object to its k nearest neighbor. |
class |
KNNWeightOutlier<O extends DatabaseObject,D extends DoubleDistance>
Outlier Detection based on the accumulated distances of a point to its k nearest neighbors. |
class |
LDOF<O extends DatabaseObject>
Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database. |
class |
LOCI<O extends DatabaseObject,D extends NumberDistance<D,?>>
Fast Outlier Detection Using the "Local Correlation Integral". |
class |
LOF<O extends DatabaseObject,D extends NumberDistance<D,?>>
Algorithm to compute density-based local outlier factors in a database based on a specified parameter LOF.K_ID (-lof.k ). |
class |
LoOP<O extends DatabaseObject>
LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability. |
class |
OPTICSOF<O extends DatabaseObject>
OPTICSOF provides the Optics-of algorithm, an algorithm to find Local Outliers in a database. |
class |
ReferenceBasedOutlierDetection<V extends NumberVector<V,N>,N extends Number>
provides the Reference-Based Outlier Detection algorithm, an algorithm that computes kNN distances approximately, using reference points. |
class |
SOD<V extends NumberVector<V,?>,D extends Distance<D>>
|
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.statistics |
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Classes in de.lmu.ifi.dbs.elki.algorithm.statistics that implement Algorithm | |
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class |
DistanceStatisticsWithClasses<V extends DatabaseObject,D extends NumberDistance<D,?>>
Algorithm to gather statistics over the distance distribution in the data set. |
class |
EvaluateRankingQuality<V extends NumberVector<V,?>,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries. |
class |
RankingQualityHistogram<V extends DatabaseObject,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.evaluation.histogram |
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Classes in de.lmu.ifi.dbs.elki.evaluation.histogram that implement Algorithm | |
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class |
ComputeOutlierHistogram<O extends DatabaseObject>
Compute a Histogram to evaluate a ranking algorithm. |
Fields in de.lmu.ifi.dbs.elki.evaluation.histogram declared as Algorithm | |
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private Algorithm<O,Result> |
ComputeOutlierHistogram.algorithm
Holds the algorithm to run. |
Fields in de.lmu.ifi.dbs.elki.evaluation.histogram with type parameters of type Algorithm | |
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private ObjectParameter<Algorithm<O,Result>> |
ComputeOutlierHistogram.ALGORITHM_PARAM
Parameter to specify the algorithm to be applied, must extend Algorithm . |
Uses of Algorithm in de.lmu.ifi.dbs.elki.evaluation.roc |
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Classes in de.lmu.ifi.dbs.elki.evaluation.roc that implement Algorithm | |
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class |
ComputeROCCurve<O extends DatabaseObject>
Compute a ROC curve to evaluate a ranking algorithm and compute the corresponding ROCAUC value. |
Fields in de.lmu.ifi.dbs.elki.evaluation.roc declared as Algorithm | |
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private Algorithm<O,Result> |
ComputeROCCurve.algorithm
Holds the algorithm to run. |
Fields in de.lmu.ifi.dbs.elki.evaluation.roc with type parameters of type Algorithm | |
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private ObjectParameter<Algorithm<O,Result>> |
ComputeROCCurve.ALGORITHM_PARAM
Parameter to specify the algorithm to be applied, must extend Algorithm . |
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