Distributed Data Mining on Data Grid Platforms

Abstract

In this paper, we present a new framework for developing novel and innovative data mining techniques to deal with very large and distributed heterogeneous datasets in both commercial and academic applications. To face with large, graphically distributed, high dimensional, multi-owner, and heterogeneous datasets, Grid can be used as data storage and computing platform to provide an effective computational support for distributed data mining applications. The main components are detailed as well as its interfaces allowing the user to efficiently develop and implement their data mining applications techniques on a Grid platform such as Globus Toolkit, DGET, etc.