RapidMiner (formerly YALE) is one of the most widely used open-source data mining suites and software solutions due to its leading-edge technologies and its functional range. Applications of RapidMiner cover a wide range of real-world data mining tasks.
Modelling Data Mining Processes as Operator Trees
The modular operator concept of RapidMiner 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 which means that they do not have to cope with the actual data format or different data views - the RapidMiner core takes care of all necessary transformations. This drastically eases the optimization of both the preprocessing and the actual data mining process.
Selection of Operators
RapidMiner (formerly YALE) and its plugins provide more than 400 operators for all aspects of Data Mining. Meta operators automatically optimize the experiment designs and users no longer need to tune single steps or parameters any longer. A huge amount of visualization techniques and the possibility to place breakpoints after each operator give insight into the success of your design - even online for running experiments.
Multi-Layered Data View Concept
RapidMiner's most important characteristic is the ability to nest operator chains and build complex operator trees. In order to support this characteristic, the RapidMiner data core acts like a data base management system and provides a multi-layered data view concept on a central data table which underlies all views. This multi-layered view concept is also an efficient way to store different views on the same data table. This is especially important for automatic data preprocessing tasks like feature generation or selection.
RapidMiner as a Data Mining IDE
All data mining processes are designed as operator trees. Unlike most other Data Mining suits, the operators in RapidMiner are not defined in a graph layout where components are positioned and connected by the user. The trees are defined in XML which turns RapidMiner into a powerful scripting language engine for data mining experiments and together with the graphical user interface into a first and complete IDE for Knowledge Discovery.
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 modelled 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
Range of Applications
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.
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Linux, Macosx, Windows, Macos, Unix
- Data Formats: None
- Tags: Large Scale, Similarity Graph, Semi Supervised Learning, Association Rules, Attribute Selection, Classification, Clustering, Preprocessing, Regression, Ensembles, Neural Nets, Kernels, Support Vector
- Archive: download here
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