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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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樓主: commingle
11#
發(fā)表于 2025-3-23 09:51:35 | 只看該作者
A High Performance Implementation of the Data Space Transfer Protocol (DSTP)tributed data mining queries. In particular, we describe 1) Osiris’s ability to lay out data by row or by column, 2) a scheduler intended to handle requests using standard network links and requests using network links enjoying some type of premium service, and 3) a mechanism designed to hide latenc
12#
發(fā)表于 2025-3-23 15:01:20 | 只看該作者
Efficient Parallel Algorithms for Mining Associationswhich utilize the temporal or sequential relationships between events. It is shown that many existing algorithms actually belong to a few categories which are decided by the broader design strategies. Overall the aim of the chapter is to provide a comprehensive account of the challenges and issues i
13#
發(fā)表于 2025-3-23 20:48:39 | 只看該作者
14#
發(fā)表于 2025-3-23 23:25:22 | 只看該作者
15#
發(fā)表于 2025-3-24 02:29:00 | 只看該作者
Parallel and Distributed Data Mining: An Introductionrom it. Data mining refers to the entire process of extracting useful and novel patterns/models from large datasets. Due to the huge size of data and amount of computation involved in data mining, high-performance computing is an essential component for any successful large-scale data mining applica
16#
發(fā)表于 2025-3-24 10:30:42 | 只看該作者
The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Projectoperative Research Centre for Advanced Computational Systems (ACSys) is a link between industry and research focusing on the deployment of high performance computers for data mining. We present an overview of the work of the ACSys Data Mining projects where the use of large-scale, high performance c
17#
發(fā)表于 2025-3-24 12:30:54 | 只看該作者
18#
發(fā)表于 2025-3-24 14:55:29 | 只看該作者
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發(fā)表于 2025-3-24 22:36:55 | 只看該作者
20#
發(fā)表于 2025-3-25 00:29:31 | 只看該作者
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