WEKA ((c) The University of Waikato, Hamilton, New Zealand) is available at this URL:
http://www.cs.waikato.ac.nz/ml/weka/
WEKA can interact with Big Data, e.g., send map-and-reduce jobs to Hadoop to train or test a classifier or a regressor in WEKA:
http://www.cs.waikato.ac.nz/ml/weka/bigdata.html
http://markahall.blogspot.co.nz/2013/10/weka-and-hadoop-part-1.html
A book about WEKA is available from the authors of this system:
Data Mining: Practical Machine Learning Tools and Techniques
It is possible to call several WEKA classes not from its graphical interface, but to call them in batch (either in the command-line or controlled by another program). E.g., to call the decision tree J48 (similar to Ross Quinlan's C4.5 decision tree) with a confidence level of 0.5 (if the enthropy of our input data allowed this confidence level):
set CLASSPATH=%CLASSPATH%;C:\WEKA\weka.jar
java weka.filters.unsupervised.attribute.Remove -i source_file.arff -R 2,5,6 > temp_file.arff
java weka.classifiers.trees.J48 -t temp_file.arff -c 4 -C 0.5 > resulting_report.txt
del temp_file.arff
(it is possible also to call WEKA through popen() or other calls), but in all cases it is necessary to know what are the available WEKA classes to be called from the command-line and their options.
The current script is for knowing all the classes that WEKA offers and the command-line options and arguments to call each of them. The script in this repository is designed for Linux/Mac OS-X/Unix.
WEKA currently has over one thousand public classes (and more than that quantity in nested classes). Some classes are graphical for which there are not command-line help, like:
weka.gui.arffviewer.ArffViewer (the ARFF file viewer)
weka.classifiers.bayes.net.GUI (Bayesian net classifier)
weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer
weka.gui.boundaryvisualizer.BoundaryVisualizer
weka.gui.DatabaseConnectionDialog
weka.experiment.PairedCorrectedTTester (setup, run, and analyze experiments)
weka.gui.experiment.DistributeExperimentPanel (distribute experiments across several hosts)
weka.gui.beans.KnowledgeFlow (the main WEKA interface -> Knowledge Flow)
weka.gui.explorer.Explorer (the WEKA Explorer)
weka.gui.GUIChooser (the main WEKA interface, like to the Explorer)
weka.gui.SimpleCLI (the WEKA command-line interface)
Some classes take other subclasses (are a batch pipeline):
weka.clusterers.ClusterEvaluation
A few classes naturally need to load slowler because all the drivers they need, like:
weka.core.converters.DatabaseLoader
WEKA can also be used naturally as a library from Java, avoiding to fork an external process. E.g., this is the documentation for the J48 decision tree invoked above:
http://weka.sourceforge.net/doc.dev/weka/classifiers/trees/J48.html
There is a simple example in this repository of a WEKA J48 decision tree in Scala (file weka_j48_decision_tree.scala).