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Colloquia Archive

Mining, Indexing, and Searching Graphs in Large Biological Data Sets by Jiawei Han

April 30, 2008

Co-sponsored by the Indiana Center for Systems Biology and Personalized Medicine and the IEEE Central Indiana Section.

Host: Jake Chen

Abstract

Recent research on pattern discovery has progressed from mining frequent itemsets and sequences to mining structured patterns including trees, lattices, and graphs. A graph can model complicated relations among data with broad applications including social network analysis, the Web, and bioinformatics. However, mining and searching large graphs is challenging because of an exponential number of frequent subgraphs.

In this talk, Jiawei Han presents gSpan and CloseGraph, efficient and scalable methods for mining frequent graph patterns in large databases. Prof. Han also introduces constraint-based graph mining methods. He next presents gIndex, a graph indexing method, and grafil, a graph approximate searching method. Both take advantages of frequent graph mining to construct a compact but highly effective graph index for similarity searches. These methods facilitate mining and querying chemical compounds and biological networks in massive datasets.

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, data streams, information network analysis, spatial data mining, and biological data mining, with more than 300 journal and conference publications. He has chaired or served on more than 100 program committees of major conferences and workshops in data mining and database systems. In addition to serving on the editorial boards of several journals, he is the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data. He is an ACM Fellow and has received the ACM SIGKDD Innovations Award (2004) and the IEEE CS Technical Achievement Award (2005). His book Data Mining: Concepts and Techniques has been adopted by many universities worldwide.