Via KDnuggets: ACM’s Special Interest Group on Knowledge Discovery and Data Mining have two interesting webinars coming up. One is on exploiting link data in data mining. The other is a tutorial on learning Bayesian networks. You can register for either event here. They’re both free and given by noted experts in those areas. More info below.
Algorithms like PageRank and HITS have been developed in late 1990s to explore links among Web pages to discover authoritative pages and hubs. Links have also been popularly used in citation analysis and social network analysis. We show that the power of links can be explored thoroughly at data mining in classification, clustering, information integration, and other interesting tasks. Some recent results of our research that explore the crucial information hidden in links will be introduced, including (1) multi-relational classification, (2) user-guided clustering, (3) link-based clustering, and (4) object distinction analysis. The power of links in other analysis tasks will also be discussed in the talk.
Jiawei Han, Professor, Department of Computer Science, University of Illinois at Urbana-Champaign. He has been working on research into data mining, data warehousing, database systems, data mining from spatiotemporal data, multimedia data, stream and RFID data, Web data, social network data, and biological data, with over 300 journal and conference publications.
He has chaired or served on over 100 program committees of international conferences and workshops, including PC co-chair of 2005 (IEEE) International Conference on Data Mining (ICDM), Americas Coordinator of 2006 International Conference on Very Large Data Bases (VLDB). He is also serving as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data. He is an ACM Fellow and has received 2004 ACM SIGKDD Innovations Award and 2005 IEEE Computer Society Technical Achievement Award. His book “Data Mining: Concepts and Techniques” (2nd ed., Morgan Kaufmann, 2006) has been popularly used as a textbook worldwide.
Please register at http://kdd.webex.com/ (webcast is free)
The second webinar…
Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. The 1990’s saw the emergence of excellent algorithms for learning Bayesian networks from passive data. In 2004 I unified this research with my text Learning Bayesian Networks. This tutorial is based on that text and my paper.
Neapolitan, R.E., and X. Jiang, “A Tutorial on Learning Causal Influences,” in Holmes, D. and L. Jain (Eds.): Innovations in Machine Learning, Springer-Verlag, New York, 2005.
I will discuss the constraint-based method for learning Bayesian networks using an intuitive approach that concentrates on causal learning. Then I will show a few real examples.
Richard E. Neapolitan is Professor and Chair of Computer Science at Northeastern Illinois University. He has previously written three books including the seminal 1990 Bayesian network text Probabilistic Reasoning in Expert Systems. More recently, he wrote the 2004 text Learning Bayesian networks, and Foundations of Algorithms, which has been translated to three languages and is one of the most widely-used algorithms texts world-wide. His books have the reputation of making difficult concepts easy to understand because of the logical flow of the material, the simplicity of the explanations, and the clear examples.