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User Rating:
Rated by: |
Very Good (4.2/5)
3 user(s) |
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RapidMiner Community Edition description
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A great data mining application filled with interesting features
RapidMiner Community Edition is a handy and comprehensive application which enables you to easily , manage the process and analyze your data.
RapidMiner Community Edition is the world-wide leading data mining solution due to the combination of its functional range and its leading-edge technologies. Applications of RapidMiner have a wide spread in data mining all over the world.
Use RapidMiner Community Edition and explore your data! Simplify the construction of experiments and the evaluation of different approaches. Here are some key features of "RapidMiner Community Edition":
· Freely available open-source data mining and analysis system
· Runs on every major platform and operating system
· Most intuitive process design
· Multi-layered data view concept ensures efficient data handling
· GUI mode, server mode (command line), or access via Java API
· Simple extension mechanism
· Powerful high-dimensional plotting facilities
· Most comprehensive solution available: more than 500 operators for data integration and transformation, data mining, evaluation, and visualization
· Automatic meta optimization schemes
· Definition of re-usable building blocks
· Standardized XML interchange format for processes
· Graphical process design for standard tasks, scripting language for arbitrary operations
· Machine learning library WEKA fully integrated
· Access to data sources like Excel, Access, Oracle, IBM DB2, Microsoft SQL, Sybase, Ingres, MySQL, Postgres, SPSS, dBase, Text files and more
· Most comprehensive data mining solution with respect to data integration, transformation, and modeling methods
· Winner of several user and jury awards
Requirements:
· Java
What's New in This Release: [ read full changelog ]
· Added an operator for performing a local polynomial regression
· Added an operator for calculating weights using a local polynomial regression.
· Added an operator for extracting the cluster centroids or prototypes from a flat cluster model.
· Added an operator for calculating the cross distances between example sets.
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