Overview (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

Algorithms
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.classification	Classification 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.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
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.
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.subspace.clique	Helper classes for the `CLIQUE` algorithm.
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.outlier	Outlier detection algorithms
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.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.
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.)

Databases and Index Structures
Package	Description
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.datastore	General data store layer API (along the lines of `Map<DBID, T>` - use everywhere!)
de.lmu.ifi.dbs.elki.database.datastore.memory	Memory data store implementation for ELKI.
de.lmu.ifi.dbs.elki.database.ids	Database object identification and ID group handling API.
de.lmu.ifi.dbs.elki.database.ids.generic	Database object identification and ID group handling - generic implementations.
de.lmu.ifi.dbs.elki.database.ids.integer	Integer-based DBID implementation -- do not use directly - always use `DBIDUtil`.
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.
de.lmu.ifi.dbs.elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries.
de.lmu.ifi.dbs.elki.database.query.range	Prepared queries for ε-range queries, that return all objects within the radius ε.
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.datasource	Data normalization (and reconstitution) of data sets.
de.lmu.ifi.dbs.elki.datasource.bundle	Object bundles - exchange container for multi-represented objects.
de.lmu.ifi.dbs.elki.datasource.filter	Data filtering, in particular for normalization and projection.
de.lmu.ifi.dbs.elki.datasource.filter.cleaning	Filters for data cleaning.
de.lmu.ifi.dbs.elki.datasource.filter.normalization	Data normalization.
de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise	Normalizations operating on columns / variates; where each column is treated independently.
de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise	Instancewise normalization, where each instance is normalized independently.
de.lmu.ifi.dbs.elki.datasource.filter.selection	Filters for selecting and sorting data to process.
de.lmu.ifi.dbs.elki.datasource.filter.transform	Data space transformations.
de.lmu.ifi.dbs.elki.datasource.filter.typeconversions	Filters to perform data type conversions.
de.lmu.ifi.dbs.elki.datasource.parser	Parsers for different file formats and data types.
de.lmu.ifi.dbs.elki.index	Index structure implementations
de.lmu.ifi.dbs.elki.index.distancematrix	Precomputed distance matrix.
de.lmu.ifi.dbs.elki.index.idistance	iDistance is a distance based indexing technique, using a reference points embedding.
de.lmu.ifi.dbs.elki.index.invertedlist	Indexes using inverted lists.
de.lmu.ifi.dbs.elki.index.lsh	Locality Sensitive Hashing
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH.
de.lmu.ifi.dbs.elki.index.lsh.hashfunctions	Hash functions for LSH
de.lmu.ifi.dbs.elki.index.preprocessed	Index structure based on preprocessors
de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics	Preprocessed index used by the FastOPTICS algorithm.
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.preprocessed.localpca	Index using a preprocessed local PCA.
de.lmu.ifi.dbs.elki.index.preprocessed.preference	Indexes storing preference vectors.
de.lmu.ifi.dbs.elki.index.preprocessed.snn	Indexes providing nearest neighbor sets
de.lmu.ifi.dbs.elki.index.projected	Projected indexes for data.
de.lmu.ifi.dbs.elki.index.tree	Tree-based index structures
de.lmu.ifi.dbs.elki.index.tree.metrical	Tree-based index structures for metrical vector spaces.
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree	Cover-tree variations.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	M-Tree and variants.
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	`MkAppTree`
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop	`MkCoPTree`
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax	`MkMaxTree`
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab	`MkTabTree`
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	`MTree`
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query	Classes for performing queries (knn, range, ...) on metrical trees.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies	Strategies for M-Trees (and variants).
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert	Insertion (choose path) strategies of nodes in an M-Tree (and variants).
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split	Splitting strategies of nodes in an M-Tree (and variants).
de.lmu.ifi.dbs.elki.index.tree.query	Classes related to generic tree queries.
de.lmu.ifi.dbs.elki.index.tree.spatial	Tree-based index structures for spatial indexing.
de.lmu.ifi.dbs.elki.index.tree.spatial.kd	K-d-tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants	R*-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	`DeLiCluTree`
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat	`FlatRStarTree`
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	`RdKNNTree`
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	`RStarTree`
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies	Various strategies for R-Trees and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk	Packages for bulk-loading R*-Trees.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert	Insertion strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow	Overflow treatment strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert	Reinsertion strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split	Splitting strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.util	Utilities for R*-Tree and variants.
de.lmu.ifi.dbs.elki.index.vafile	Vector Approximation File
de.lmu.ifi.dbs.elki.persistent	Persistent data management.

Datatypes and Distance Functions
Package	Description
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.projection	Data projections.
de.lmu.ifi.dbs.elki.data.spatial	Spatial data types - interfaces and utilities.
de.lmu.ifi.dbs.elki.data.synthetic	Generators for synthetic data sets
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.data.uncertain	Uncertain data objects.
de.lmu.ifi.dbs.elki.data.uncertain.uncertainifier	Classes to generate uncertain objects from existing certain data.
de.lmu.ifi.dbs.elki.distance	`distance functions` and `similarity functions`.
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.colorhistogram	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.external	Distance functions using external data sources.
de.lmu.ifi.dbs.elki.distance.distancefunction.geo	Geographic (earth) distance functions.
de.lmu.ifi.dbs.elki.distance.distancefunction.histogram	Distance functions for one-dimensional histograms.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski	Minkowski space L_p norms such as the popular Euclidean and Manhattan distances.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence.
de.lmu.ifi.dbs.elki.distance.distancefunction.set	Distance functions for binary and set type data.
de.lmu.ifi.dbs.elki.distance.distancefunction.strings	Distance functions for strings.
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace	Distance functions based on subspaces.
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries	Distance functions designed for time series.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster	Similarity measures for comparing clusters.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.

Evaluation
Package	Description
de.lmu.ifi.dbs.elki.evaluation	Functionality for the evaluation of algorithms.
de.lmu.ifi.dbs.elki.evaluation.classification	Evaluation of classification algorithms.
de.lmu.ifi.dbs.elki.evaluation.classification.holdout	Holdout and cross-validation strategies for evaluating classifiers.
de.lmu.ifi.dbs.elki.evaluation.clustering	Evaluation of clustering results.
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments	Pair-segment analysis of multiple clusterings.
de.lmu.ifi.dbs.elki.evaluation.histogram	Functionality for the evaluation of algorithms using histograms.
de.lmu.ifi.dbs.elki.evaluation.index	Simple index evaluation methods
de.lmu.ifi.dbs.elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model.
de.lmu.ifi.dbs.elki.evaluation.scores	Evaluation of rankings and scorings.
de.lmu.ifi.dbs.elki.evaluation.scores.adapter	Adapter classes for ranking and scoring measures.
de.lmu.ifi.dbs.elki.evaluation.similaritymatrix	Render a distance matrix to visualize a clustering-distance-combination.

GUI and Visualization
Package	Description
de.lmu.ifi.dbs.elki.gui	Graphical User Interfaces for ELKI
de.lmu.ifi.dbs.elki.gui.configurator	Configurator components
de.lmu.ifi.dbs.elki.gui.icons	Icons for ELKI GUI.
de.lmu.ifi.dbs.elki.gui.minigui	A very simple UI to build ELKI command lines.
de.lmu.ifi.dbs.elki.gui.multistep	Multi-step GUI for ELKI
de.lmu.ifi.dbs.elki.gui.multistep.panels	Panels for the multi-step GUI
de.lmu.ifi.dbs.elki.gui.util	Utility classes for GUIs (e.g. a class to display a logging panel)

Utilities and Miscellaneous
Package	Description
de.lmu.ifi.dbs.elki	ELKI framework "Environment for Developing KDD-Applications Supported by Index-Structures".
de.lmu.ifi.dbs.elki.application	Base classes for stand alone applications.
de.lmu.ifi.dbs.elki.application.cache	Utility applications for the persistence layer such as distance cache builders.
de.lmu.ifi.dbs.elki.application.experiments	Packaged experiments to make them easy to reproduce.
de.lmu.ifi.dbs.elki.application.geo	Application for exploring geo data.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.application.internal	Internal utilities for development.
de.lmu.ifi.dbs.elki.application.jsmap	JavaScript based map client - server architecture.
de.lmu.ifi.dbs.elki.logging	Logging facility for controlling logging behavior of the complete framework.
de.lmu.ifi.dbs.elki.logging.progress	Progress status objects (for UI)
de.lmu.ifi.dbs.elki.logging.statistics	Classes for logging various statistics.
de.lmu.ifi.dbs.elki.math	Mathematical operations and utilities used throughout the framework.
de.lmu.ifi.dbs.elki.math.analysis	Analysis package
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.geodesy
de.lmu.ifi.dbs.elki.math.geometry	Algorithms from computational geometry.
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.fitting	Function to numerically fit a function (such as a `Gaussian distribution` to given data.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions	Weight functions used in weighted PCA via `WeightedCovarianceMatrixBuilder`
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections	Random projection families.
de.lmu.ifi.dbs.elki.math.random	Random number generation.
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.math.statistics	Statistical tests and methods.
de.lmu.ifi.dbs.elki.math.statistics.dependence	Statistical measures of dependence, such as correlation.
de.lmu.ifi.dbs.elki.math.statistics.distribution	Standard distributions, with random generation functionalities.
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator	Estimators for statistical distributions.
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta	Meta estimators: estimators that do not actually estimate themselves, but instead use other estimators, e.g. on a trimmed data set, or as an ensemble.
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality	Methods for estimating the intrinsic dimensionality.
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions	Kernel functions from statistics.
de.lmu.ifi.dbs.elki.math.statistics.tests	Statistical tests.
de.lmu.ifi.dbs.elki.parallel	Parallel processing core for ELKI.
de.lmu.ifi.dbs.elki.parallel.processor	Processor API of ELKI, and some essential shared processors.
de.lmu.ifi.dbs.elki.parallel.variables	Variables are instantiated for each thread, and allow passing values from one processor to another within the same thread.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
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.result.textwriter.naming	Naming schemes for clusters (for output when an algorithm doesn't generate cluster names).
de.lmu.ifi.dbs.elki.result.textwriter.writers	Serialization handlers for individual data types.
de.lmu.ifi.dbs.elki.utilities	Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
de.lmu.ifi.dbs.elki.utilities.datastructures	Basic memory structures such as heaps and object hierarchies.
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike	Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.
de.lmu.ifi.dbs.elki.utilities.datastructures.arrays	Utilities for arrays: advanced sorting for primitvie arrays.
de.lmu.ifi.dbs.elki.utilities.datastructures.hash	Hashing based data structures.
de.lmu.ifi.dbs.elki.utilities.datastructures.heap	Heap structures and variations such as bounded priority heaps.
de.lmu.ifi.dbs.elki.utilities.datastructures.hierarchy	Delegate implementation of a hierarchy.
de.lmu.ifi.dbs.elki.utilities.datastructures.histogram	Classes for computing histograms.
de.lmu.ifi.dbs.elki.utilities.datastructures.iterator	ELKI Iterator API.
de.lmu.ifi.dbs.elki.utilities.documentation	Documentation utilities: Annotations for Title, Description, Reference
de.lmu.ifi.dbs.elki.utilities.ensemble	Utility classes for simple ensembles.
de.lmu.ifi.dbs.elki.utilities.exceptions	Exception classes and common exception messages.
de.lmu.ifi.dbs.elki.utilities.io	Utility classes for input/output.
de.lmu.ifi.dbs.elki.utilities.optionhandling	Parameter handling and option descriptions.
de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints	Constraints allow to restrict possible values for parameters.
de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization	Configuration managers See the `de.lmu.ifi.dbs.elki.utilities.optionhandling` package for documentation!
de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters	Classes for various typed parameters.
de.lmu.ifi.dbs.elki.utilities.pairs	Pairs utility classes.
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	Scaling functions: linear, logarithmic, gamma, clipping, ...
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.utilities.xml	XML and XHTML utilities.
de.lmu.ifi.dbs.elki.workflow	Work flow packages, e.g. following the usual KDD model, closely related to CRISP-DM

Tutorial Code and Examples
Package	Description
tutorial	Classes for ELKI tutorials.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.distancefunction	Classes from the tutorial on implementing distance functions.
tutorial.javaapi	Examples how to invoke ELKI from Java.
tutorial.outlier

ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures.

ELKI is a generic framework for a broad range of KDD-applications and their development. For background, contact-information, and contributors see http://elki.dbs.ifi.lmu.de.

This is the documentation for version 0.7, published as:
Erich Schubert, Andreas Koos, Tobias Emrich, Andreas Züfle, Klaus Arthur Schmid, Arthur Zimek:
A Framework for Clustering Uncertain Data.
Proceedings of the VLDB Endowment, 8(12): 1976–1979, 2015.

Getting started

The ELKI wiki has additional documentation and will continuously be updated. A Tutorial exported from the Wiki is included with this documentation and a good place to start.

Invocation

To use the KDD-Framework we recommend an executable .jar-file: elki.jar. Since release 0.3 it will by default invoke a minimalistic GUI called MiniGUI when you call java -jar elki.jar. For command line use (for example for batch processing and scripted operation), you can get a description of usage by calling java -jar elki.jar KDDCLIApplication -h.

The MiniGUI can also serve as a utility for building command lines, as it will print the full command line to the log window.

For more information on using files and available formats as data input see de.lmu.ifi.dbs.elki.datasource.parser. ELKI uses a whitespace separated vector format by default, but there also is a parser for ARFF files included that can read most ARFF files (mixing sparse and dense vectors is currently not allowed).

An extensive list of parameters can be browsed sorted by class or sorted by option ID.

Some examples of completely parameterized calls for different algorithms are described at example calls.

A list of related publications, giving details on many implemented algorithms, can be found in the class article references list.

Workflow - Where Do Which Objects Go?

The database connection manages reading of input files or databases and provides a Database-Object - including index structures - as a virtual database to the KDDTask. The KDDTask applies a specified algorithm on this database and collects the result from the algorithm. Finally, KDDTask hands on the obtained result to a ResultHandler. The default-handler is ResultWriter, writing the result to STDOUT or, if specified, into a file.

Database and indexing layer

The database and indexing layer is a key component of ELKI. This is not just a storage for double[], as with many other frameworks. It can store various types of objects, and the integrated index structures provide access to fast distance, similarity, kNN, RkNN and range query methods for a variety of distance functions.

The standard flow for initializing a database is as depicted here:
Database initialization
The standard stream-based data sources such as FileBasedDatabaseConnection will open the stream, feed the contents through a Parser to obtain an initial MultipleObjectsBundle. This is a temporary container for the data, which can then be modified by arbitrary ObjectFilters.
In the end, the MultipleObjectsBundle is bulk-inserted into a Database, which will then invoke its IndexFactorys to add Index instances to the appropriate relations.

When a request for a distance, similarity, kNN, RkNN or range query is received by the database, it queries all indexes if they have support for this query. If so, an optimized query is returned, otherwise a linear scan query can be returned unless DatabaseQuery.HINT_OPTIMIZED_ONLY was given.

For this optimization to work, you should be using the proper APIs of the Database interface or QueryUtil helper where possible, instead of initializing low level classes such as an explicit linear scan query.

For efficiency, try to instantiate the query only once per algorithm run, and avoid running the optimization step for every object.

How to make use of this framework

Extension

To provide new applications one is simply to implement the specified interfaces. There are interfaces for a broad range of targets of development. Compare the tree of interfaces to get an overview concerning the provided interfaces.

A good place to get started is to have a look at some of the existing algorithms, and see how they are implemented. For example the DummyAlgorithm while it does not produce any result, will teach you how to perform k-nearest-neighbor queries properly. It does however have a hard dependency on the Euclidean distance and the datatypes supported by it. In order to support arbitrary distance functions, extend the class AbstractDistanceBasedAlgorithm instead. This is another simple example, this time for obtaining a class parameter.

Visit the ELKI Wiki, which has a growing amount of documentation. You are also welcome to contribute, of course!

Parameterization API

ELKI is designed for command-line, GUI and Java operation. For command-line and GUI, an extensive help functionality is provided along with input assistance. Therefore, you should also support the parameterizable API. The requirements are quite different from regular Java constructors, and cannot be expressed in terms of a Java API.

For useful error reporting and input assistance in the GUI we need to have more extensive typing than Java uses (for example we might need numerical constraints) and we also want to be able to report more than one error at a time. In ELKI 0.4, much of the parameterization was refactored to static helper classes usually found as a public static class Parameterizer and subclasses of AbstractParameterizer.

Parameterization

Keep the complexity of Parameterizer classes and constructors invoked by these classes low, since these may be heavily used during the parameterization step. Postpone any extensive initialization to the main algorithm invocation step!