Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

You need to increase the JVM heap size. Use the Option -Xmx ( where count is the size of the desired heap... ) on your command line to augment the size of the heap. Example: java -Xmx400M weka.classifiers.j48.J48 -t data/trainingset.arff -d treeclassifier.model

Note: it is also possible to augment the size of the stack. And note also that you can write -Xmx200M to get a heap of a size of 200000000 byte. Use ``java -h'' and ``java -X'' to see al the JVM options.

Source: Weka FAQ