Statistical Modeling of Relational Data

author: Pedro Domingos, Dept. of Computer Science & Engineering, University of Washington
published: Aug. 12, 2007,   recorded: August 2007,   views: 19379


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Part 1 59:44
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Part 2 09:51
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Part 3 1:31:22


KDD has traditionally been concerned with mining data from a single relation. However, most applications involve multiple interacting relations, either explicitly (in relational databases) or implicitly (in semi-structured and multimodal data). Examples include link analysis, social networks, bioinformatics, information extraction, security, ubiquitous computing, etc. Mining such data has become a topic of keen interest in the KDD community in recent years. The key difficulty is that data in relational domains is no longer i.i.d. (independent and identically distributed), greatly complicating statistical modeling. However, research has now advanced to the point where robust, easy-to-use, general-purpose techniques and languages for mining non-i.i.d. data are available. The goal of this tutorial is to add a sufficient subset of these concepts and techniques to the toolkits of both researchers and practitioners.

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Reviews and comments:

Comment1 Jue, August 17, 2007 at 11:34 p.m.:

This is really an terrific tutorial. I wish I was there.

Comment2 Fei, August 18, 2007 at 7:13 p.m.:

Interesting and important topic, nice presentation!

Comment3 Alireza, January 13, 2008 at 5:42 a.m.:

How can we save this lecture on our PC?
Best Regards,

Comment4 Francisco Pereira, November 23, 2016 at 11:04 a.m.:


Is there some problem on lecture 3, minute 28:43?... It just stops for me and never moves... :-(


Comment5 Amos Mulievi, January 18, 2017 at 4:23 a.m.:

Great material

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