分布式数据挖掘Distributed Data Mining |
|
课程网址: | http://videolectures.net/acai05_fatta_ddm/ |
主讲教师: | Giuseppe di Fatta |
开课单位: | 德国康斯坦茨大学 |
开课时间: | 2007-02-25 |
课程语种: | 英语 |
中文简介: | 数据挖掘是对大量数据的自动分析,寻找数据中隐含的关系和知识。大量数据的数据挖掘和知识发现可以受益于并行和分布式计算环境的使用,以提高数据选择的性能和质量。本教程的目标是向研究人员和实践者介绍如何利用高性能并行和分布式计算技术挖掘大型数据集。本教程分为两部分。第一部分介绍了高性能并行和分布式计算。分析了数据挖掘技术和算法中可以利用的不同形式的并行性。第二部分回顾了分布式数据挖掘方法。针对每种数据挖掘技术,提出并讨论了不同的并行实现方法。此外,还讨论了并行和分布式数据挖掘系统和算法。最后,概述了高性能数据挖掘的研究现状和前景。 |
课程简介: | Data mining is the automated analysis of large volumes of data looking for relationships and knowledge that are implicit in data. Data mining and knowledge discovery in large amounts of data can benefit from the use of parallel and distributed computational environments to improve both performance and quality of data selection. The goal of this tutorial is to provide researchers and practitioners with an introduction to mining large data sets by exploiting techniques from high performance parallel and distributed computing. This tutorial is organized in two parts. In the first part an introduction to high performance parallel and distributed computing is provided. Different forms of parallelism that can be exploited in data mining techniques and algorithms are analyzed. The second part presents a review of distributed data mining approaches. For each data mining technique, different ways for parallel implementation are presented and discussed. Furthermore, parallel and distributed data mining systems and algorithms are discussed. Finally, current research issues and perspectives in high-performance data mining are outlined. |
关 键 词: | 分布式; 数据挖掘 |
课程来源: | 视频讲座网 |
最后编审: | 2019-11-02:lxf |
阅读次数: | 68 |