jiawei han - Computer Science

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Nov 20, 2013 (3 years and 8 months ago)


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Mining and Searching Graphs in Biological Databases

Jiawei Han

Department of Computer Science

University of Illinois at Urbana


October 4
, 2005

3:00PM 110

Kresge Library


Recent research on pattern discovery has progressed from mining frequent item sets and sequences
to mining structured patterns including trees, lattices, and graphs. As a general data structu
re, graph
can model complicated relations among data with wide applications in bioinformatics. However,
mining and searching large graphs in graph databases 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
and searching of graphs in 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 super
graph of g that has the same support as g. After that, we introduce a graph
indexing method, gIndex and a graph approximate searching method, grafil, both taking advantages
of frequent graph mining to construct a compact but highly effective graph index a
nd perform
similarity search with such indexing structures. These methods facilitate mining and querying graph
patterns in large biological databases. Our performance study shows the high promise of our


Jiawei Han, Professor, Department o
f Computer Science, University of Illinois at Urbana
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 biological
databases, with over 300 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 Co
nferences on Data
Engineering (PC vice
chair), 2005 International Conferenceon 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, IEEE
Transactions on Knowledge and Data
Engineering, and Journal of Intelligent Information Systems and the Editor
Cheif of ACM
Transactions on Knoweldge Discovery from Data. He is an ACM Fellow and has received 2004 ACM
SIGKDD Innovations Award. His textb
ook "Data Mining: Concepts and Techniques" (Morgan
Kaufmann, 2001) has been popularly used for data mining courses in universities