Distributed Data Mining Models as Services on the Grid

This paper describes how distributed data mining models, such as collective learning, ensemble learning, and meta-learning models, can be implemented as WSRF mining services by exploiting the Grid infrastructure. Our goal is to design a general distributed architectural model that can be exploited for different distributed mining algorithms deployed as Grid services for the analysis of dispersed data sources. In order to validate our approach, we present also the implementation of two clustering algorithms on such architecture, and evaluate their performance.