RapidMiner x64 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.

RapidMiner x64 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 and explore your data! Simplify the construction of experiments and the evaluation of different approaches. Try to find the best combination of preprocessing and learning steps or let RapidMiner do that automatically for you.

FEATURES:

· 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.

New Release
New Features:
Added new operators to delete data from Azure Cloud:
Delete Azure Blob Storage Resource
Delete Azure Data Lake Storage Resource
Delete Azure Data Lake Storage Gen2 Resource
Enhancements:
All Loop cloud operators (e.g. Loop Amazon S3, Loop Azure Blob Storage, etc) now only download a file when another operator reads its content. The memory footprint may also decrease by 50%, and unnecessary writes to the disk are avoided.
Bugfixes:
Continue RapidMiner Studio start if proxy discovery fails
Added missing Cluster attribute to metadata when applying a KMeans model via Apply Model
Fixed a regression in Generalized Linear Model (GLM) model training. It again accepts weighted training data
Auto Model Clustering showed incorrect results, ignoring training data normalization and attribute reordering
Fixed AbstractMethodError when using very old JDBC drivers (built for Java 6 and earlier) to connect to SQL databases.
[ RapidMiner Community Edition x64 release history ]

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