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COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER
AND THE SCHOOL OF PHYSICS, ASTRONOMY, & COMPUTATIONAL SCIENCES
(CSI 898-Sec 001)
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Time Series Data Mining and Pattern Discovery
Jessica Lin
Computer Science Department, George Mason University, Fairfax, VA
Massive amounts of data are generated daily at a rapid rate. As a result,
the world is faced with unprecedented challenges and opportunities on
managing the ever-growing data, and much of the world's supply of data is
in the form of time series. One obvious problem of handling time series
databases concerns with its typically massive size. Most classic data
mining algorithms do not perform or scale well on time series data due to
their unique structure. In particular, the high dimensionality, very high
feature correlation, and the typically large amount of noise that
characterize time series data present a difficult challenge. As a result,
time series data mining has attracted an enormous amount of attention in
the past two decades.
This presentation gives an overview of my contributions in the field of
time series data mining. The first part of the presentation discusses
time series data mining fundamentals - more specifically, the two aspects
that hugely determine the efficiency and effectiveness of most time series
data mining algorithms: data representation and similarity measure. The
second part of the presentation will focus on the discovery of novel and
non-trivial patterns in time series data, including frequently encountered
(or repeated) patterns, rare (or anomalous) patterns, contrasting patterns
and latent structure.
March 23, 2015
4:30 pm
Exploratory Hall, room 3301, Fairfax Campus
Refreshments will be served at 4:15 PM.
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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm