Mining and Analysis of Information Networks

Speaker:	Professor Philip S. YU
		University of Illinois at Chicago

Title:		"Mining and Analysis of Information Networks"

Date:		Tuesday, 24 May 2011

Time:		10:30am-11:30am

Venue:		Room 3416 (via lifts 17/18), HKUST

Abstract:

In an interconnected world, the evolution of one entity may cause a series
of significant value changes of some others. For example, the currency
inflation of Thailand caused the slumping currencies of other Asian
countries, which finally lead to the financial crisis in 1997. We will
call these entities with high impacts shakers. We'll discuss the problem
of how to discover shakers through a novel concept of construction of a
cascading graph to capture the causality relationships among the evolving
entities over some period of time, and then infer shakers through the
graph. Next we consider the problem of using the network approach to
provide a more efficient approach to solve the top-k maximal frequent
pattern mining problem. This is achieved through building a pattern graph
from the transaction database after some initial fast processing.
Different from traditional bottom up strategies such as level-wise or
tree-growth mining approaches, the graph based method works in a top-down
manner. It can pull large maximal cliques from the pattern graph and
directly use such large-sized maximal cliques as promising candidates for
long frequent patterns. This greatly reduces the execution time compared
to the traditional bottom up approaches.


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Biography:

Philip S. Yu received the B.S. Degree in E.E. from National Taiwan
University, the M.S. and Ph.D. degrees in E.E. from Stanford University,
and the M.B.A. degree from New York University. He is currently a
Professor in the Department of Computer Science at the University of
Illinois at Chicago and also holds the Wexler Chair in Information
Technology.   He spent most of his career at IBM Thomas J. Watson Research
Center and was manager of the Software Tools and Techniques group. His
research interests include data mining, privacy preserving data
publishing, data stream, Internet applications and technologies, and
database systems. Dr. Yu has published more than 620 papers in refereed
journals and conferences. He holds or has applied for more than 300 US
patents.

Dr. Yu is a Fellow of the ACM and the IEEE.  He is the Editor-in-Chief of
ACM Transactions on Knowledge Discovery from Data.  He is on the steering
committee of the IEEE Conference on Data Mining and ACM Conference on
Information and Knowledge Management and was a member of the IEEE Data
Engineering steering committee.  He was the Editor-in-Chief of IEEE
Transactions on Knowledge and Data Engineering (2001-2004). He had also
served as an associate editor of ACM Transactions on the Internet
Technology and Knowledge and Information Systems.  He had received several
IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding
Technical Achievement Award, 2 Research Division Awards and the 94th
plateau of Invention Achievement Awards.  He was an IBM Master Inventor.
Dr. Yu received a Research Contributions Award from IEEE Intl. Conference
on Data Mining in 2003 and also an IEEE Region 1 Award for "promoting and
perpetuating numerous new electrical engineering concepts" in 1999.