Andrew's Leap 2007 Lecture
DATA MINING USING FRACTALS
("fractals for fun and profit")
Christos Faloutsos (CMU)
FOILS
(ppt, internal to CMU)
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.