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Titlebook: Applied Parallel Computing: Advanced Scientific Computing; 6th International Co Juha Fagerholm,Juha Haataja,Ville Savolainen Conference pro

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樓主: BRISK
41#
發(fā)表于 2025-3-28 16:53:22 | 只看該作者
42#
發(fā)表于 2025-3-28 20:58:31 | 只看該作者
43#
發(fā)表于 2025-3-28 23:51:05 | 只看該作者
A Data Mining Architecture for Clustered Environmentsibed system architecture for scalable and portable data mining architecture for clustered environment. The architecture contains modules for secure safe-thread communication, database connectivity, organized data management and efficient data analysis for generating global mining model.
44#
發(fā)表于 2025-3-29 07:09:08 | 只看該作者
Automated Fitting and Rational Modeling Algorithm for EM-Based S-Parameter Data full-wave electro-magnetic simulations. The adaptive algorithm doesn’t require any a priori knowledge of the dynamics of the system to select an appropriate sample distribution and an appropriate model complexity.
45#
發(fā)表于 2025-3-29 07:19:59 | 只看該作者
46#
發(fā)表于 2025-3-29 12:32:05 | 只看該作者
Aufbruch zu Beginn der 60er Jahre, warehouses and large databases is to integrate data mining with OLAP in DSS. Parallel and distributed processing are also two important components of successful large-scale data mining applications. In this paper, a high performance data mining scheme is proposed. The overall architecture and the mechanism of the system are described.
47#
發(fā)表于 2025-3-29 17:15:16 | 只看該作者
48#
發(fā)表于 2025-3-29 21:35:47 | 只看該作者
https://doi.org/10.1007/978-3-658-42798-6uch as rule induction, clustering algorithms, decision trees, genetic algorithms, and neural networks, the possible ways to exploit parallelism are presented and discussed in detail. Finally, some promising research directions in the parallel data mining research area are outlined.
49#
發(fā)表于 2025-3-30 00:14:56 | 只看該作者
50#
發(fā)表于 2025-3-30 05:30:32 | 只看該作者
Parallelism in Knowledge Discovery Techniquesuch as rule induction, clustering algorithms, decision trees, genetic algorithms, and neural networks, the possible ways to exploit parallelism are presented and discussed in detail. Finally, some promising research directions in the parallel data mining research area are outlined.
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