RapidMiner Community Edition 9.9.0 freeware
RapidMiner 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.
|OS||Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10|
|Installation||Instal And Uninstall|
|Keywords||data miner, business intelligence, data analyzer, analyzer, analyze, checker|
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|9.9.0||Mar 24, 2021||New Release||New Features:
Data is the central piece in any RapidMiner process. The way RapidMiner internally deals with data has fundamentally changed in this release with the new Data Core (codename Belt). Its new columnar table representation provides a quantum leap in processing speed and memory efficiency for RapidMiner processes. Multiple operators already use it internally and it becomes fully available now for extension developers to create fast and efficient operators.
Added a Set Positive Value operator for the new Data Core which can make nominal attributes binominal or change the positive value of binominal attributes
Replaced the Rename by Example Values operator by a new and improved version
Replaced the Rename operator by a new one that can additionally handle a renaming dictionary
Replaced the Sort operator by one that can sort by multiple attributes (currently already part of the Operator Toolbox extension)
|9.8.1||Dec 3, 2020||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
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.
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.
|9.8.0||Oct 14, 2020||New Release||New Features:
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.
Time Series Windowing Update:
Added time based (window parameters are specified in time units) and custom windowing (start and stop values of the windows are provided by an additional example set) for all windowing operators (Windowing, Process Windows, Forecast Validation, Sliding Window Validation)
Added a few more parameters: expert settings (couples a few expert parameters into not shown, if it is not selected), windows defined (specifies from which point windows are defined), empty window handling.