可扩展的链接挖掘和信息网络分析Scalable Link Mining and Analysis on Information Networks |
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课程网址: | http://videolectures.net/ilpmlgsrl09_yu_slmain/ |
主讲教师: | Philip S. Yu |
开课单位: | 伊利诺大学 |
开课时间: | 2009-09-18 |
课程语种: | 英语 |
中文简介: | 随着信息网络的普及及其广泛的应用,人们对多学科信息网络的构建、在线分析处理和挖掘进行了大量的研究,其中包括社会网络分析、万维网、数据库系统、数据挖掘、机器学习和网络通信,以及信息系统。像pagerank和hits这样的算法已经在20世纪90年代后期开发出来,用来探索网页之间的链接,以发现权威页面和中心。链接在引文分析和社交网络分析中也得到了广泛的应用。然而,对于如何充分挖掘链接在可伸缩数据分析中的作用还缺乏系统的研究。在本文中,我们将详细研究链接的功能,以提高典型数据分析任务(包括信息集成、在线分析处理和其他有趣的数据挖掘任务)的效率和效率,特别是在多关系数据库和/或万维网环境中。 |
课程简介: | With the ubiquity of information networks and their broad applications, there have been numerous studies on the construction, online analytical processing, and mining of information networks in multiple disciplines, including social network analysis, World-Wide Web, database systems, data mining, machine learning, and networked communication and information systems. Algorithms like PageRank and HITS have been developed in late 1990s to explore links among Web pages to discover authoritative pages and hubs. Links have also been popularly used in citation analysis and social network analysis. However, there is a lack of systematic treatment on how to fully explore the power of links in scalable data analysis. In this talk, the power of links are examined in details to improve the effectiveness and efficiency of typical data analysis tasks, including information integration, on-line analytic processing, and other interesting data mining tasks, especially in the multi-relational databases and/or the World-Wid e Web environments. |
关 键 词: | 信息网络; 数据库系统; 数据挖掘; 机器学习; 网络通信 |
课程来源: | 视频讲座网 |
最后编审: | 2020-07-16:yumf |
阅读次数: | 60 |