WEKA (the Waikato Environment for Knowledge Analysis) is an open
source and freely available workbench for applying machine learning
techniques to practical problems, integrating many different machine
learning tools within a common framework and a uniform, if basic but
functional, user interface. WEKA incorporates over 60 machine
learning techniques, ranging from traditional decision trees,
association rules, clustering, through to modern random forests and
support vector machines. A WEKA user is able to use machine learning
techniques to derive useful knowledge from quite large databases.
Typical users include both researchers and industrial scientists.
From University of Waikato
WEKA is a great suite of data mining algorithms that allow us to
quickly explore alternatives approaches to data mining. However,
perhaps because it is written in Java, it is well known that the WEKA
user interface is very heavy in its use of memory. Staying with the
command line in WEKA (see the excellent guide written by Alexander
K. Seewald) is a good option with reports of, for example, using the
rather efficient NaiveBayesNominal algorithm to process a large
ham/spam dataset with 500,000 samples and 1.3 million attributes on
the commandline ``in a few minutes.'' (See KDD Nuggets, 2007, n24).
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