Decision tree construction for data mining on grid computing environments

In this paper, we present our Grid-based decision tree architecture, with the intention of applying it to both parallel and sequential algorithms. Also, we show that, based on the scope and model of data mining applied in the Grid environment as well as user equivalent perspective, Grid roles can be categorized into three types. It is our goal, through these definitions, to help software developers define clear system processes and differentiate the application scope for software applications. To fulfill our architecture, we first apply an existing parallel decision tree algorithm (the SPRINT algorithm) to the Grid environment. The performance and differences in many other areas are compared using datasets of different sizes. The experimental results will be used for future reference and further development.