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Performing massive data mining experiments with multiple datasets and methodsis a common task faced by most bioinformatics and computational biologylaboratories. WEKA is a machine learning package designed to facilitate thistask by providing tools that allow researchers to select from severalclassification methods and specific test strategies. Despite its popularity,the current WEKA environment for batch experiments, namely Experimenter, hasfour limitations that impact its usability: the selection of value ranges formethods options lacks flexibility and is not intuitive; there is no support forparallelisation when running large-scale data mining tasks; the XML schema isdifficult to read, necessitating the use of the Experimenter's graphical userinterface for generation and modification; and robustness is limited by thefact that results are not saved until the last test has concluded.  FlexDM implements an interface to WEKA to run batch processing tasks in asimple and intuitive way. In a short and easy-to-understand XML file, one candefine hundreds of tests to be performed on several datasets. FlexDM alsoallows those tests to be executed asynchronously in parallel to take advantageof 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 weencourage other researchers to explore and adopt our software, FlexDM is madeavailable as a pre-configured bootable reference environment. All code,supporting documentation and usage examples are also available for download athttp://sourceforge.net/projects/flexdm.

FlexDM: Enabling robust and reliable parallel data mining using WEKA