Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI) is a data mining software framework developed for use in research and teaching.
ELKI is a framework that provides algorithms for clustering, managing database indexes and outlier detection.
In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation. This separation makes ELKI unique among data mining frameworks like Weka or YALE and frameworks for index structures like GiST.
The fundamental approach is the independence of file parsers or database connections, data types, distances, distance functions, and data mining algorithms. Helper classes, e.g. for algebraic or analytic computations are available for all algorithms on equal terms.
- Minimalistic graphical user interface for interactive parameterization of ELKI algorithms.
- Data mining algorithms such as k-means variations, outlier detection ensembles.
- Visualize results:
- Outlier Scores.
- Clustering results.
- ROC Curves.
- OPTICS plots.
- Index MBRs.
- Parallel coordinates.
- Voronoi cells.
- Alpha shapes.
- Cluster differences.
- Index structures (various R-tree splitting and bulk loading strategies).
- Evaluation methods (various clustering similarity measures).
- Spatial outlier detection visualization on geographical data.
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