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Data Mining using Fractals and Power laws

Dr. Christos Faloutsos
Carnegie Mellon University

Date:  Friday, October 14, 2005
Time: 11:00-12:00 p.m
Place: 1260 Anthony Hall
Host: R. Jin

Abstract: What patterns can we find in a bursty web traffic? On the web or on the internet graph itself? How about the distributions of galaxies in the sky, or the distribution of a company's customers in geographical space? How long should we expect a nearest-neighbor search to take, when there are 100 attributes per patient or customer record? The traditional assumptions (uniformity, independence, Poisson arrivals, Gaussian distributions), often fail miserably. Should we give up trying to find patterns in such settings? Self-similarity, fractals and power laws are extremely successful in describing real datasets (coast-lines, rivers basins, stock-prices, brain-surfaces, communication-line noise, to name a few). We show some old and new successes, involving modeling of graph topologies (internet, web, and social networks); modeling galaxy and video data; dimensionality reduction; and more.

Biography: Christos Faloutsos holds a Ph.D. degree in Computer Science from the University of Toronto, Canada. He is currently a professor at Carnegie Mellon University . He has received the Presidential Young Investigator Award by the National Science Foundation (1989), five ``best paper'' awards, and four teaching awards. He has published over 130 refereed articles, one monograph, and holds four patents. His research interests include data mining, fractals, indexing in relational and multimedia databases, and database performance.