Statistical Modeling of Relational Data
published: Aug. 12, 2007, recorded: August 2007, views: 19367
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !