Introduction to Graphical Models for Data Mining by Arindam Banerjee, Department of Computer Science and Engineering, University of Minnesota.
Abstract:
Graphical models for large scale data mining constitute an exciting development in statistical data analysis which has gained significant momentum in the past decade. Unlike traditional statistical models which often make `i.i.d.’ assumptions, graphical models acknowledge dependencies among variables of interest and investigate inference/prediction while taking into account such dependencies. In recent years, latent variable Bayesian networks, such as latent Dirichlet allocation, stochastic block models, Bayesian co-clustering, and probabilistic matrix factorization techniques have achieved unprecedented success in a variety of application domains including topic modeling and text mining, recommendation systems, multi-relational data analysis, etc. The tutorial will give a broad overview of graphical models, and discuss recent developments in the context of mixed-membership models, matrix analysis models, and their generalizations. The tutorial will present a balanced mix of models, inference/learning methods, and applications.
If you plan on using data mining as a source for authoring topic maps, graphical models are on your reading list.
Questions:
- Would you use the results of a Bayesian network to author an entry in a topic map? Why/why not? (2-3 pages, no citations)
- Would you use the results of a Bayesian network to author an entry in a library catalog? Why/why not? (2-3 pages, no citations)
- Do we attribute certainty to library catalog entries that are actually possible entries for a particular item? (discussion question)
- Examples of the use of Bayesian networks in classification for library catalogs?