The modular operator concept of RapidMiner (formerly YALE) allows the
design of complex nested operator chains for a huge number of learning
problems in a very fast and efficient way (rapid prototyping).
The data handling is transparent to the operators.
They do not have to cope with the actual data format or different data
views - the RapidMiner core takes care of all necessary transformations.
Read here about the most important features of RapidMiner.
Knowledge Discovery in Databases (KDD) is a complex and demanding task.
While a large number of methods has been established for numerous problems,
many challenges remain to be solved.
Rapid prototyping is an approach which allows crucial design decisions as
early as possible.
A rapid prototyping system should support maximal re-use
and innovative combinations of existing methods, as well as simple and quick
integration of new ones.
The main features of RapidMiner are:
- freely available open-source knowledge discovery environment
- 100% pure Java (runs on every major platform and operating system)
- KD processes are modeled as simple operator trees
which is both intuitive and powerful
- operator trees or subtrees can be saved as building blocks
for later re-use
- internal XML representation ensures standardized interchange format
of data mining experiments
- simple scripting language allowing for automatic large-scale experiments
- multi-layered data view concept ensures efficient and transparent data handling
- Flexibility in using RapidMiner:
- graphical user interface (GUI) for interactive prototyping
- command line mode (batch mode) for automated large-scale applications
- Java API (application programming interface) to ease usage of RapidMiner
from your own programs
- simple plugin and extension mechanisms, a broad variety of plugins
already exists and you can easily add your own
- powerful plotting facility offering a large set of sophisticated
high-dimensional visualization techniques for data and models
- more than 400 machine learning, evaluation, in- and output,
pre- and post-processing, and visualization operators plus numerous meta optimization schemes
- machine learning library WEKA fully integrated
(WEKA web page)
- RapidMiner was successfully applied on a wide range of applications where its
rapid prototyping abilities demonstrated their usefulness, including
text mining, multimedia mining,
feature engineering, data stream mining
and tracking drifting concepts, development of
ensemble methods, and distributed data mining.
For a summary of all available RapidMiner operators please refer to the
Operator Overview page.