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Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code.
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Open data mining platform. Provides common architecture for algorithms of various types. Efficient processing of arbitrarily large volumes of data thanks to data streaming. Weka and Rseslib partially integrated. (www.debellor.org)
This project is meant to be an complete reformulation with extensions to a Machine Learning Software, the Weka, from Waikato University. The goal is to make Weka oriented by the CRISP-DM process, with all features accessible by web. On Web technologies, this is a integration of JSF, Hibernate and Weka.
ML# (ML-Sharp) is a machine learning toolkit created in C# 3.0. It is similar to the Weka library for Java, and it is able to consume some Weka functionality via interop through IKVM. ML# has numerous advantages over Weka including a cleaner API and faster run-time execution.
The csv2arff project is Python converter from the CSV file format to the ARFF file format. The latter is the data format used in the Weka Machine Learning software (http://www.cs.waikato.ac.nz/ml/weka/). Two version of the tool are planned: a command line version and a GUI version.