Category: Operators
Extensions that provide new operators not falling into any of the other categories.
Do you want to plot datasets to HTML5 chart png images and create a GIF from these images? Check this extension with two operators for just the tasks described. Simply use your Studio chart config to create beautiful animated plots.
This extension contains a number of operators designed for converting different IOObjects into other useful representations.
This extension allows to build new operators out of processes without any coding and bundle them into extensions.
This extension provides various data search and integration methods for enriching (extending) a data table, using a heterogenous tabular corpus. These include Correspondence Search (Search-Join for single attribute) including human-in-the-loop refinements, Unconstrained and Correlation Search.
Some operators of this extension require a Data Search server developed by University of Mannheim, which maintains the public enpoints.
This extension is part of the ISGAT project.
It provides the functionality to create Data Schema and adapt user data to the Data Schema.
The Data Watermarking extension contains operators to add a watermark to data and validate a watermark to ensure data integrity.
Some data processing operations missing from RapidMiner core, but available in SQL databases.
This extension adds the fuzzy matching algorithm as an operator and as a function to the
expression builder.
Visually define data prep or ETL workflows and execute them directly in the database. Reduce data transfer by loading only the data you need after preparation.
Extension to use data from Linked Open Data in RapidMiner, both as input to the data mining process as well as for adding background knowledge to existing problem.
The Mannheim RapidMiner toolbox contains operators from various categories; ranging from slightly modified implementations of original operators to new preprocessing and learning operators.
Operators provided by this plugin allow user to display scatterplot visualizations of data together with their models and therefore perform a sensitivity analysis of the models. This allows human user to understand especially the blackbox models better - this includes complex models such as neural nets, however, the plugin is suitable for any PredictionModel.
The optimization plugin for RapidMiner aims to introduce mathematical optimization based learning capability to RapidMiner. An unconstrained optimization problem, which is called learning or training in machine learning can be solved with different algorithms.
The Python scripting extension integrates RapidMiner with the data scientist-friendly and widely used programming language Python and allows to embed Python code into RapidMiner processes.
The R scripting extension integrates RapidMiner with the well-known statistical language R and allows to embed R scripts into RapidMiner processes. Requires Studio 6.4 or newer!
Unleash the full power of cryptography directly from within RapidMiner!
The Finance and Economics Extension for RapidMiner gives you quick and easy access to over 150,000 finance and economic time series data sets and more. You can also transform and analyze the data using various financial operators included in the the operator set. Integrate stock, index and other time series easily into your RapidMiner workflow.
RapidMiner Radoop is a code-free environment for designing advanced analytic processes that push computations down to your Hadoop cluster. You need a specific license to use this product.
This extension adds two new set of operators to RapidMiner which can be used for item recommendation and rating prediction.
This extension adds new operators to RapidMiner which can be used to generate reports consisting of tables and visualizations generated by RapidMiner.
This extension provides operators for processing time series and includes a variety of preprocessing steps as well as various methods for extracting features from value series.
This extension enables user to apply Shapelet analysis on their time series data.
For more information see X. Renard, M. Rifqi, G. Fricout, M. Detyniecki : EAST representation: fast discovery of discriminant temporal patterns from time series, ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Riva Del Garda, Italy (2016)
The extension adds Operators to design and deploy streaming analytic process on Flink or Spark.
This is a beta version. Please use it carefully, as features are still under development.
This operator does approximate string matching for the each string present in the example set with the text present in document.