Data mining-based fragmentation of XML data warehouses
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Fragmenting very large XML data warehouses via K-means clustering algorithm
International Journal of Business Intelligence and Data Mining
Combining software agents and grid middleware
GPC'07 Proceedings of the 2nd international conference on Advances in grid and pervasive computing
Data mining on desktop grid platforms
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
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The increasing availability of clusters and grids of workstations provides cheap and powerful ressources for distributed datamining. To exploit these ressources we need new algorithms adapted to this kind of environment, in particular with respect to the way to fragment data and to use this fragmentation. An "intelligent" distribution of data is required and can be obtained from clustering. Most existing parallel methods of clustering are developped for supercomputers with shared memory and hence can not be used on a Grid. This paper presents a new clustering algorithm, called Progressive Clustering, which executes a clustering in an efficient and incremental distributed way. The data clusters resulting from this algorithm can subsequently be used in distributed data mining tasks.