RapidMiner

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For other uses of Yale, see Yale (disambiguation).

RapidMiner (formerly YALE (Yet Another Learning Environment)) is an environment for machine learning and data mining experiments. It allows experiments to be made up of a large number of arbitrarily nestable operators and they are described in XML files which can easily be created with RapidMiner's graphical user interface. Applications of RapidMiner cover both research and real-world data mining tasks. Its initial version was developed by the Artificial Intelligence Unit of Dortmund University since 2001. It has a GNU license, and is hosted by SourceForge since 2004. RapidMiner provides more than 400 operators for all main machine learning procedures, including input and output, and data preprocessing and visualization. It is written in the Java programming language and therefore can work on all popular operating systems. It also integrates all learning schemes and attribute evaluators of the Weka learning environment.

Properties

A RapidMiner screenshot (click for full size view).

Some properties of RapidMiner are:

written in Java
knowledge discovery processes are modeled as operator trees
internal XML representation ensures standardized interchange format of data mining experiments
scripting language allows for automatic large-scale experiments
multi-layered data view concept ensures efficient and transparent data handling
graphical user interface, command line mode (batch mode), and Java API for using RapidMiner from your own programs
plugin and extension mechanisms, several plugins already exist
plotting facility offering a large set of high-dimensional visualization schemes for data and models
applications include text mining, multimedia mining, feature engineering, data stream mining and tracking drifting concepts, development of ensemble methods, and distributed data mining.

References

Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.