Pattern
mining is one of the fundamental techniques in data mining. As one
increases the complexity of the pattern types one discovers
potentially more informative patterns. In this talk I will look
at some of the traditional and emerging pattern mining tasks, which
span sets, sequences, trees and graphs. I will highlight applications
of these complex patterns in bioinformatics, such as discovery of
co-operating transcription factors via structured motifs, extracting
common RNA sub-structures, consensus evolutionary trees, and
structural motifs through tree & graph mining, mining patterns of gene
expression, and extracting protein folding intermediates.
Bio:
Mohammed J. Zaki is an Associate Professor of Computer Science at RPI.
He received his Ph.D. degree in computer science from the University
of Rochester in 1998. His research interests focus on developing novel
data mining techniques and their applications, especially for
bioinformatics. He has published over 150 papers on data mining, and
co-edited several books. He is currently an associate editor for IEEE
Transactions on Knowledge and Data Engineering, action editor for Data
Mining and Knowledge Discovery, and on several editorial boards. He is
a recipient of the NSF CAREER Award (2001) and DOE ECPI Award
(2002).
