Mining Heterogeneous Information Networks By Exploring the Power of Links
(invited lecture for DS 2009)
Author: Jiawei Han
Department of Computer Science,
University of Illinois at Urbana-Champaign, USA
Knowledge is power but for interrelated data, knowledge is often
hidden in massive links in heterogeneous information networks. We
explore the power of links at mining information networks in several
interesting tasks including link-based information integration, truth
validation, clustering, and integrated clustering and ranking.
Some recent results of our research that explore the crucial
information hidden in links will be introduced, including (1) object
distinction analysis, (2) veracity analysis, and (3) RankClus:
integrated clustering and ranking. We also discuss some of our
on-going studies in this direction.
Jiawei Han is a Professor at the 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
400 journal and conference publications.
He has chaired or served on over 100 program committees of
international conferences and workshops, including PC co-chair for
KDD, SDM, and ICDM conferences, and Americas Coordinator for a VLDB
conference. He is also serving as the founding Editor-In-Chief of ACM
Transactions on Knowledge Discovery from Data.
He is a Fellow of ACM and IEEE, 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
©Copyright 2009 Author