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The decision tree-based classification is a popular approach for pattern recognition and data mining. Most decision tree induction methods assume training data being present at one central location. Given the growth in distributed databases at geographically dispersed locations, the methods for decision tree induction in distributed settings are gaining importance. This paper describes one distributed...
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...
Advances in computing and communication has resulted in very large scale distributed environments in recent years. They are capable of storing large volumes of data and often have multiple compute nodes. However, the inherent heterogeneity of data components, the dynamic nature of distributed systems, the need for information synchronization and data fusion over a network and security and access control...
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