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What is the WhiBo plugin for Rapidminer?


Today’s guest post about an awesome new plugin for Rapidminer, is from Milan Vukicevic.  Although I walked in at the very end of his presentation at RCOMM 2010, I sat down with Milan on my last day and he gave me a personal demo of WhiBo.  The applications I see from this plugin, as it relates to the financial world, is its ability to build algorithms on new data,  find patterns, and tweak parameters that were never possible before. Thanks Milan!

WhiBo is a RapidMiner plug-in for component-based design and performance testing of data mining algorithms. Users can design whole algorithms simply by connecting components. These components are building blocks that represent crucial algorithmic steps that every algorithm of certain type should have.

WhiBo has an interactive GUI for design of component-based algorithms that can be designed and saved for reuse with just a few clicks, without having to write a single line of code. This way, data mining practitioners have more possibilities to construct and rebuild algorithms that better adapt to concrete data.

In comparison with traditional algorithms, which could only be adjusted by parameter tuning, this approach offers more significant possibilities of algorithm adjustment. A component repository for design and testing of Decision tree and Partitioning Clustering algorithms is provided. This repository allows users to design algorithms which can outperform traditional, well-known, algorithms. If needed, component-based design allows simple extension of the repository, but also definitions of new generic algorithms (e.g. neural networks, SVMs etc.). When combined with RapidMiner’s pre-processing and visualization operators, WhiBo becomes a powerful tool for pattern recognition and predictive analysis.

For more information about WhiBo and component-based approach in design and application of data mining algorithms, feel free to contact me at milan.vukicevic AT, (remove AT). Installation instructions, detailed user and developer documentation and list of our publications can be found on

Up next Using the SVM RBF Kernel Wow, I’m happy to announce that today is the first of a two part guest post series. Today’s guest post is by Marin Matijas, who gave a Rapidminer 5.0 Video Tutorial 11 - Pattern Recognition and Landmarking Plugin I’m back to making new videos again, at least for a little while! This new video showcases the Pattern Recognition & Landmarking plugin that
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