You are viewing the RapidMiner Studio documentation for version 9.8 - Check here for latest version
What's new in RapidMiner Studio 9.8
Large files in Projects
Utilize AI Hub 9.8 support for large files in Projects. Files with more than 10MB and stored ExampleSets are automatically handled to be versioned as expected, but stored more efficiently. This is backed by Git LFS, which means Python or R coders can continue to easily work with these projects as long as they have the Git LFS extension installed.
K-Means clustering using H2O
The new K-Means (H2O) operator implements the popular clustering algorithm using the bundled H2O library. It provides useful features such as estimating the value of k and built-in standardization and nominal encoding to enable RapidMiner Studio users to quickly prototype clustering models. The algorithm proves to be fast and quite memory efficient even for large datasets.
Improved Windowing for Time Series
Added time based and custom windowing for all windowing operators.
Improved Proxy Support
Incorporated a new library to better make use of system proxy settings if System is selected in the preferences, especially w.r.t. Windows and WPAD/PAC files. This will drastically improve the experience in complex corporate network setups.
Enhancements and bug fixes
The following pages describe the enhancements and bug fixes in RapidMiner Studio 9.8 releases: