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New Algorithms for Data Mining on Grid Computing

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Data Mining on the grid is a new challenge which consists of processing a large quantity of data by knowledge discovery methods. New methods which are able to take into consideration all the specificities of data Mining problems as well as those of grid computing (data grid) are needed for a better  exploitation of the treatment of available resources in grid computing. In this paper, we have proposed a method for the extraction of association rules, adapted for parallel and distributed environment such as grids. The goal here is to minimize the execution time by reducing the cost of parallelization and communication. A parallel version of the Partition algorithm for finding association rules has been proposed. The introduction of an intelligent distribution of the base on the grid using clustering to minimize the search space in each machine and consequently reduce the processing time has been considered. To this end, another parallel clustering algorithm for treating numerical and categorical data in a distributed environment has been proposed.
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Association Rules; Clustering; Distributed Data Mining; Partition; K-Prototypes; Parallel Algorithm

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