Title: Meta-learning in grid-based data mining systems

Authors: Moez Ben Haj Hmida, Yahya Slimani

Addresses: Department of Computer Science, Faculty of Sciences of Tunis, Campus Universitaire, 2092 El Manar, Tunis, Tunisia. ' Department of Computer Science, Faculty of Sciences of Tunis, Campus Universitaire, 2092 El Manar, Tunis, Tunisia

Abstract: The Weka4GML framework has been designed to meet the requirements of distributed data mining. In this paper, we present the Weka4GML architecture based on WSRF technology for developing meta-learning methods to deal with datasets distributed among data grid. This framework extends the Weka toolkit to support distributed execution of data mining methods, like meta-learning. The architecture and the behaviour of the proposed framework are described in this paper. We also detail the different steps needed to execute a meta-learning process on a Globus environment. Finally, the framework has been discussed and compared to related works.

Keywords: distributed data mining; meta-learning; grid computing; distributed datasets; web service resource framework; WSRF.

DOI: 10.1504/IJCNDS.2010.034945

International Journal of Communication Networks and Distributed Systems, 2010 Vol.5 No.3, pp.214 - 228

Available online: 31 Aug 2010

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