Data Mining using Fractals and Power
laws
Dr. Christos
Faloutsos
Carnegie Mellon University
Date:
Time:
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