Mining Graph and Structured Patterns in Biological Databases
Speaker: Professor Jiawei Han, University of Illinois at Urbana-Champaign
When: 2004-06-02 13:00:00
Venue: 78-420
Host: Xiaofang Zhou
Abstract:Recent research on pattern discovery has progressed from mining
frequent itemsets and sequences to mining structured patterns
including trees, lattices, and graphs. As a general data structure,
graph can model complicated relations among data with wide
applications in bioinformatics. However, mining large graph
patterns is challenging due to the presence of an exponential number
of frequent subgraphs.
In this talk, we present our recent progress on developing efficient
and scalable methods for mining graph patterns from large biological
databases. We first introduce gSpan, an efficient method for mining
all the frequent graph patterns in graph databases, by extension of
a depth-first frequent pattern growth method, developed in our
previous research. Then we introduce CloseGraph, an efficient
method for mining closed frequent graph patterns. A graph g is
closed in a database if there exists no proper supergraph of g that
has the same support as g. Finally, we introduce a graph indexing
method, which takes an advantage of frequent graph mining to
construct a compact but highly effective graph index. These methods
facilitate mining and querying graph patterns in large biological
databases. Our performance study shows the high promise of our
approach.
Biography: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, spatial and
multimedia databases, deductive and object-oriented databases, Web
databases, and bio-medical databases, with over 250 journal and
conference publications. He has chaired or served in many program
committees of international conferences and workshops, including
2001 and 2002 SIAM-Data Mining Conference (PC co-chair), 2004 and
2002 International Conferences on Data Engineering (PC vice-chair),
2005 International Conference on Data Mining (PC co-chair), ACM
SIGKDD conferences, and ACM SIGMOD conferences. He also served or is
serving on the editorial boards for Data Mining and Knowledge
Discovery: An International Journal, IEEE Transactions on Knowledge
and Data Engineering, and Journal of Intelligent Information
Systems. He is an ACM Fellow since 2003. His textbook "Data
Mining: Concepts and Techniques" (Morgan Kaufmann, 2001) has been
popularly used for data mining courses in universities.
Type: DKE
Contact:Xiaofang Zhou, seminar host (zxf@itee.uq.edu.au)
or Guido Governatori (ITEE seminar co-ordinator)
(guido@itee.uq.edu.au)