Invited speaker : Mohammed J. ZAKI

Talk : Towards Generic Pattern Mining

Frequent pattern mining is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this talk I will discuss the design and implementation of generic data structures and algorithms to handle various pattern types like itemsets, sequences, trees and graphs. One of the main attractions of a generic paradigm is that the generic algorithms for mining are guaranteed to work for any pattern type. Each pattern has a list of properties it satisfies, and a generic algorithm can utilize these properties to speed up the mining. I will also discuss the opportunities and challenges for formal concept analysis in generic pattern mining.

Short biography

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 for intelligence applications, bioinformatics, performance mining, web mining, and so He has published over 100 papers on data mining; co-edited 11 books, including the recent book "Data Mining in Bioinformatics" (Springer-Verlag, 2004); served as guest-editor for several journals; and has served on the program committees of major international conferences and has co-chaired many workshops (BIOKDD, HPDM, DMKD) in data mining. He is currently an associate editor for IEEE Transactions on Knowledge and Data Engineering, Int'l Journal of Data Warehousing and Mining, and ACM SIGMOD Digital Symposium Collection. He received the National Science Foundation CAREER Award in 2001 and the Department of Energy Early Career Principal Investigator Award in 2002. He also received the ACM Recognition of Service Award in 2003 on.