  Performing massive data mining experiments with multiple datasets and methods
is a common task faced by most bioinformatics and computational biology
laboratories. WEKA is a machine learning package designed to facilitate this
task by providing tools that allow researchers to select from several
classification methods and specific test strategies. Despite its popularity,
the current WEKA environment for batch experiments, namely Experimenter, has
four limitations that impact its usability: the selection of value ranges for
methods options lacks flexibility and is not intuitive; there is no support for
parallelisation when running large-scale data mining tasks; the XML schema is
difficult to read, necessitating the use of the Experimenter's graphical user
interface for generation and modification; and robustness is limited by the
fact that results are not saved until the last test has concluded.
  FlexDM implements an interface to WEKA to run batch processing tasks in a
simple and intuitive way. In a short and easy-to-understand XML file, one can
define hundreds of tests to be performed on several datasets. FlexDM also
allows those tests to be executed asynchronously in parallel to take advantage
of multi-core processors, significantly increasing usability and productivity.
Results are saved incrementally for better robustness and reliability.
  FlexDM is implemented in Java and runs on Windows, Linux and OSX. As we
encourage other researchers to explore and adopt our software, FlexDM is made
available as a pre-configured bootable reference environment. All code,
supporting documentation and usage examples are also available for download at
http://sourceforge.net/projects/flexdm.
