Project details for ELKI

Logo ELKI 0.5.0

by erich - July 1, 2012, 20:58:25 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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:

Primary release goals:

  • Cluster evaluation: metrics and circle-segment-visualization (ICDE 2012)

  • Outlier detection ensembles (SDM 2011, 2012)

  • Usability improvements, for example by adding an automatic evaluation helper

  • Performance improvements by reducing boxing of primitive types

  • Parallel coordinates visualizations added for high-dimensional data

  • Tons of new algorithms, distance functions, index structures, visualizations, evaluators, ...

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Platform Independent
Data Formats: Arff, Other, Csv, Parser Extension Api
Tags: Clustering, Visualization, Algorithms, Evaluation, Anomaly Detection, Outlier Detection, Index Structures
Archive: download here


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