Description:

ELKI: "Environment for Developing KDD-Applications Supported by Index-Structures" is a development framework for data mining algorithms written in Java. It includes a large variety of popular data mining algorithms, distance functions and index structures.

Its focus is particularly on clustering and outlier detection methods, in contrast to many other data mining toolkits that focus on classification. Additionally, it includes support for index structures to improve algorithm performance such as R*-Tree and M-Tree.

The modular architecture is meant to allow adding custom components such as distance functions or algorithms, while being able to reuse the other parts for evaluation.

This package also includes the source code, since this software is meant for the rapid development of such algorithms, not so much for end users.

Changes to previous version:

The full changelog is not yet up. Here is an excerpt of the new functions in 0.5.0 - further speed improvements - R-Tree flexibility: multiple new split strategies, bulk loaders, insertion strategies, so that ELKI can now do many R-Tree variations, including the original Guttman R-Tree, not only the R*-Tree. - K-Means flexibility: MacQueen and Lloyd style iterations along with various seeding strategies, including K-Means++ - VA-File (static only, not dynamic databases); partial-VA to come for 0.5.0 final? - Many popular cluster evaluation measures - Alpha shapes, Voronoi cells, Delaunay triangulations in the visualization layer (in the projected space, so 2D!) - Parallel coordinates (only halfway reviewed in beta1, more to come!) - Outlier ensemble code, to be presented at SDM 2012 end of april

For the final 0.5.0 release we hope to have some approximate outlier detection methods for you (aLOCI, HilOut) as well as some subspace outlier detection methods including HiCS (ICDE 2012, to be presented tomorrow).

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Various, Parser Api

Tags: Clustering, Visualization, Algorithms, Evaluation, Anomaly Detection, Outlier Detection, Index Structures

Archive: download here

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