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:

New beta release, including some new algorithms (ODIN, PINN, full O(n^3) Hierarchical Clustering, new cluster extraction methods from hierarchies), new index structures (in-memory k-d tree, LSH, projected indexes, PINN), new visualizations and much more.

This release requires Java 7, for the new visualizations also JOGL will be needed.

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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