Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Distributed data clustering can be efficient and exact
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Collective, Hierarchical Clustering from Distributed, Heterogeneous Data
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
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The increasing availability of clusters and grids of workstations allows to bring cheap and powerful ressources for distributed datamining. This paper deals with high performance search of association rules. It proposes to built an “intelligent” database fragmentation and distribution by using a prealable clustering step, a new method called Incremental clustering allows to execute this clustering step in an efficient distributed way.