Actionable Mining of Large, Multi-relational Data using Localized Predictive Models

Thursday, March 24, 2011
7:00 PM
Free and open to the public

Many large datasets associated with modern predictive data mining applications are quite complex and heterogeneous, possibly involving multiple relations, or exhibiting a dyadic nature with associated "side-information". For example, one may be interested in predicting the preferences of a large set of customers for a variety of products, given various properties of both customers and products, as well as past purchase history, a social network on the customers, and a conceptual hierarchy on the products. This talk will introduce a broad framework for effectively tackling such scenarios using a simultaneous problem decomposition and modeling strategy that can exploit the wide variety of information available. The properties and capabilities of this framework will be illustrated on several real life applications.

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Joydeep Ghosh

Schlumberger Centennial Chair Professor of Electrical and Computer Engineering
The University of Texas at Austin