A common misconception is that the Weka machine learning software cannot be applied to large datasets. This page has some information on how to handle big data with Weka (see also here). Note that the main problem lies in training models from large datasets, not prediction for large datasets. Weka is being used to make predictions in real time in very demanding real-world applications. This can be done with almost all Weka models once they have been built.
It is correct that it may be impossible to train models from large datasets using the Weka Explorer graphical user interface, even when the Java heap size has already been increased, because the Explorer always loads the entire dataset into the computer's main memory and also incurs significant overhead due to visualisation, etc.
When dealing with large datasets, it is best to either employ a command-line interface (CLI) to interact with Weka (e.g. a shell that comes with the computer's operating system or the SimpleCLI included in Weka), use Weka's Knowledge Flow graphical user interface, or write code directly in Java or a Java-based scripting language such as Groovy or Jython. Using these methods, it is possible to deal with larger datasets and even datasets that are too big to fit into main memory.
Most Weka classifiers require the entire dataset to be loaded into memory for training, but there are also schemes that can be trained in an incremental fashion, namely all classifiers implementing the weka.classifiers.UpdateableClassifier interface. These are limited in number; however, for Weka 3.8, there is a library that provides access to the MOA data stream software containing state-of-the-art algorithms for large datasets or data streams. Note also that non-incremental learning algorithms can be applied to large datasets by subsampling the data. Reservoir sampling is an incremental sampling method implemented in Weka that can be used for this purpose.
Weka 3.8 also provides access to new packages for
distributed data mining. The first new package is
called distributedWekaBase. It
provides base "map" and "reduce" tasks that are not tied to any
specific distributed platform. A second,
provides Hadoop-specific wrappers and jobs for these base
tasks. A third, called distributedWekaSpark,
provides Spark-specific wrappers.
Read more on the hadoop support...
and more on the Spark support....