Description:

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.

Main Features

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

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

Comments

Erna Maier (on January 7, 2008, 19:05:55)

RapidMiner is the most flexible and comprehensive data mining tool around.

Frank Hagemann (on March 15, 2008, 08:56:13)

RapidMiner offers an amazing functionality for rapidly desgning and automatically optimizing even large and nested and very complex data mining processes. It is a powerful tool for experts that want to get the most out of data mining in a reasonably short amount of time. RapidMiner covers the full data mining process from data loading over pre-processing, data mining process design, modelling, automated parameter optimization, automated feature selection and generation, evaluation, visualization, and deployment. RapidMiner can be used interactively via its easy-to-use graphical user interface (GUI). The GUI supports breakpoints, online visualisations while optimization are running, graphics and results exports, etc. RapidMiner can also be used on servers through its command line version. RapidMiner can also be used as a data mining, text mining, machine learning, and predictive analytics library for your own programs. It is probably one of the most complete data mining libraries. It provides more than 400 data mining operations of its own plus the about 100 data mining operations of Weka, i.e. more than 500 in total. From my point of view it is the best tool on the market. Hope this review is helpful for somebody. Best wishes, Frank

Frank Xavier (on November 7, 2008, 04:36:48)

With more than 500 data mining, pre-processing, visualisation, and evaluation operators/modules for the complete data mining process and Weka fully integrated, RapidMiner probably is one of the most comprehensive data mining solutions available. It has significantly matured over the years, i.e. scalability, robustness, and usability for complex real-world data mining tasks are met better than by any other open source data mining tool I know.

Eik Kern (on April 22, 2009, 01:41:39)

RapidMiner provides an enormous amount of flexibility and functionality. I no longer need SAS, SPSS, Clementine, and the like and the support of provided by the RapidMiner community and the Rapid-I team is amazing. Give it a try and you will never want to miss it again.

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