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Titlebook: Large-Scale Parallel Data Mining; Mohammed J. Zaki,Ching-Tien Ho Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 2000 classi

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Active Mining in a Distributed Settingimilarity Discovery, can be re-architected so that they maintain valid mined information across i) database updates, and ii) user interactions in a client-server setting, while minimizing the amount of data re-accessed.
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A Data-Clustering Algorithm on Distributed Memory Multiprocessorsodes, and essentially linear scaleup in the size of the data set and in the number of clusters desired. For a 2 gigabyte test data set, our implementation drives the 16 node SP2 at more than 1.8 gigaflops.
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Parallel Sequence Mining on Shared-Memory Machiness can be solved independently on each processor requiring no synchronization. However, dynamic inter-class and intra-class load balancing must be exploited to ensure that each processor gets an equal amount of work. Experiments on a 12 processor SGI Origin 2000 shared memory system show good speedup and excellent scaleup results.
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