Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Communications of the ACM
Early experiences with the GridFTP protocol using the GRB-GSIFTP library
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Distributed data mining on the grid
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Developing Distributed Data Mining Implementations for a Grid Environment
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
The Design of Discovery Net: Towards Open Grid Services for Knowledge Discovery
International Journal of High Performance Computing Applications
Adapting the weka data mining toolkit to a grid based environment
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
A clustering method to distribute a database on a grid
Future Generation Computer Systems
Grid-enabling data mining applications with DataMiningGrid: An architectural perspective
Future Generation Computer Systems
DMGrid: A Data Mining System Based on Grid Computing
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Efficient Grid-Based Video Storage and Retrieval
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
Webservices oriented data mining in knowledge architecture
Future Generation Computer Systems
A semi-supervised clustering algorithm based on rough reduction
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
APHID: An architecture for private, high-performance integrated data mining
Future Generation Computer Systems
Executing association rule mining algorithms under a Grid computing environment
Proceedings of the Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
Efficient algorithms for frequent pattern mining in many-task computing environments
Knowledge-Based Systems
A virtual mart for knowledge discovery in databases
Information Systems Frontiers
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Current business processes often use data from several sources. Data is characterized to be heterogeneous, incomplete and usually involves a huge amount of records. This implies that data must be transformed in a set of patterns, rules or some kind of formalism, which helps to understand the underlying information. The participation of several organizations in this process makes the assimilation of data more difficult. Data mining is a widely used approach for the transformation of data to useful patterns, aiding the comprehensive knowledge of the concrete domain information. Nevertheless, traditional data mining techniques find difficulties in their application on current scenarios, due to the complexity previously mentioned. Data Mining Grid tries to fix these problems, allowing data mining process to be deployed in a grid environment, in which data and services resources are geographically distributed, belong to several virtual organizations and the security can be flexibly solved. We propose both a novel architecture for Data Mining Grid, named DMGA, and the implementation of this architecture, named WekaG.