面包屑 发表于 2025-3-28 16:53:22
http://reply.papertrans.cn/17/1601/160012/160012_41.pnginvulnerable 发表于 2025-3-28 20:58:31
http://reply.papertrans.cn/17/1601/160012/160012_42.pngCapitulate 发表于 2025-3-28 23:51:05
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.bourgeois 发表于 2025-3-29 07:09:08
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.predict 发表于 2025-3-29 07:19:59
http://reply.papertrans.cn/17/1601/160012/160012_45.png纯朴 发表于 2025-3-29 12:32:05
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.Congregate 发表于 2025-3-29 17:15:16
http://reply.papertrans.cn/17/1601/160012/160012_47.pngIRS 发表于 2025-3-29 21:35:47
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.Insufficient 发表于 2025-3-30 00:14:56
http://reply.papertrans.cn/17/1601/160012/160012_49.pngPresbycusis 发表于 2025-3-30 05:30:32
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.