Statistics 36-350: Data Mining (Fall 2009)
Important update, December 2011 If you are looking for the latest version of this class, it is 36-462, taught by Prof. Tibshirani in the spring of 2012. 36-350 is now the course number for Introduction to Statistical Computing. Data mining is the art of extracting useful patterns from large bodies of data; finding seams of actionable knowledge in the raw ore of information. The rapid growth of com
tutorials [Auton Lab]
By: Jeff Schneider, Mary Soon Lee, Andrew Moore Description: A tutorial on using the Windows Vizier software, which provides a GUI for doing fast and autonomous locally weighted learning and nearest neighbor style learning. The Vizier software itself is available for free download at http://www.cs.cmu.edu/~awm/vizier, where you will find more details. Description: This is a link to the Auton Codin
Large graph mining: patterns, tools and case studies
Large graph mining: patterns, tools and case studies Tutorial proposal for CIKM 2008, Napa Valley, California Christos Faloutsos and Hanghang Tong, Carnegie Mellon University Abstract How do graphs look like? How do they evolve over time? How can we find patterns, anomalies and regularities in them? How to find influential nodes in the network? We will present both theoretical results and algorith
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Statistics 36-350: Data Mining, Fall 2006
Leman Akoglu, Heinz College
