网格计算环境下的分布式数据挖掘

为了提高分布式挖掘系统的性能，分析了现有的分布式数据挖掘系统的不足，提出了一种网格计算环境下的分布式数据挖掘的体系结构，讨论了在该体系结构下如何进行数据挖掘．该体系结构是面向服务的，跨平台的，在该体系结构中，挖掘算法和目标数据源被定义为web服务资源，在需要进行数据挖掘的时候，这些web服务资源被动态地、松耦合地联接在一起，共同完成一次数据挖掘任务．最后，借助网格工具globus toolkit4．0构建了一个局域网网格计算环境，并使用一个关联规则挖掘实例详细地说明了在该体系结构下的挖掘过程，

Distributed Data Mining on the Grid

Distributed data mining(DDM) is widely used in industrial,scientific and commercial applications to analyze large datasets maintained over geographically distributed sites,which makes DDM a major research issue on(today's) data mining field.This paper discusses the disadvantages of existing DDM systems,puts forward a service-oriented architecture for DDM on the grid and describe the process of mining under this framework.In the proposed,serviceoriented,cross-platform framework,the mining algorithm and distributed data sets are packed into the Web Service Resources(WS-Resource) respectively,which can cooperate to perform DDM as required dynamically.Finally,a grid based on local area network was built with Globus Toolkit 4.0 and take an example of association rule mining to illustrate how to work with the proposed architecture.