ACM Computing Surveys (CSUR)
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Distributed data clustering can be efficient and exact
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Distributed data mining on the grid
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Automatic Subspace Clustering of High Dimensional Data
Data Mining and Knowledge Discovery
Design and implementation of a data mining grid-aware architecture
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Webservices oriented data mining in knowledge architecture
Future Generation Computer Systems
Future Generation Computer Systems
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Clusters and grids of workstations provide available resources for data mining processes. To exploit these resources, new distributed algorithms are necessary, particularly concerning the way to distribute data and to use this partition. We present a clustering algorithm dubbed Progressive Clustering that provides an ''intelligent'' distribution of data on grids. The usefulness of this algorithm is shown for several distributed datamining tasks.