An environment for developing KDD-applications supported by index structures
At the same time, ELKI is open to arbitrary data types, distance or similarity measures, or file formats.
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.
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What's New in This Release:
- New release with a focus on outlier detection methods and visualization, along with some general refactoring that improves memory efficiency and performance and a minimalistic GUI for interactive parameterization of ELKI algorithms.