ELKI

You can download precompiled ELKI releases from the home page, or you can use standard Java dependency management such as Gradle and Maven. On the other hand, efficient data management tools like index-structures can show considerable impact on data mining tasks and are therefore useful for a broad variety of algorithms. 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 Rapidminer and frameworks for index structures like GiST. 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. Beginners may want to start at the HowTo documents, Examples and Tutorials to help with difficult configuration scenarios and beginning with ELKI development. This website serves as community development hub and task tracker for both bug reports, Tutorials, FAQ, general issues and development tasks. The most important documentation pages are: Tutorial, JavaDoc), FAQ, InputFormat, DataTypes, DistanceFunctions, DataSets, Development, Parameterization, Visualization, Benchmarking, and the list of Algorithms and RelatedPublications. You can download ELKI including source code on the Releases page. ELKI uses the AGPLv3 License, a well-known open source license. There is a list of Publications that accompany the ELKI releases. When using ELKI in your scientific work, you should cite the publication corresponding to the ELKI release you are using, to give credit. This also helps to improve the repeatability of your experiments. We would also appreciate if you contributed your algorithm to ELKI to allow others to reproduce your results and compare with your algorithm (which in turn will likely get you citations). We try to document every publication used for implementing ELKI: the page RelatedPublications is generated from the source code annotations. ELKI is quite fast (see some of our benchmark results) but the focus lies on a broad coverage of algorithms and variations . We discourage cross-platform benchmarking, because it is easy to produce misleading results by comparing apples and oranges. For fair comparability, you should implement all algorithms within ELKI, and use the same APIs. We have also observed Java JDK versions have a large impact on the runtime performance. To make your results reproducible, please cite the version you have been using. See also Benchmarking. You can browse the open bug reports or create a new bug report. We also appreciate any comments, suggestions and code contributions. You can contact the core development team by e-mail: elki () dbs ifi lmu de Eclipse can build ELKI, and the easiest way is to use elki-bundle as classpath, which includes everything enabled.