Initiative in Innovative Computing @ Harvard
Seminar Series
Wednesday, February 14, 2007; 4:00pm
60 Oxford Street, Room 330
Eamonn Keogh, Associate Professor, Computer Science & Engineering
Department, University of California - Riverside
Seminar Title: Algorithms and Representations for Mining Massive
Collections of Time Series and Shapes
Abstract
To date, the vast majority of research on time series and shape data
mining has focused on similarity search and clustering. I believe
that these problems should now be regarded as essentially solved. In
particular, there are now fast exact techniques for searching and
clustering patterns under both the Euclidean distance and Dynamic
Time Warping, the two most useful distance measures. However, from a
knowledge discovery viewpoint, there are much more interesting
problems, the detection of previously /unknown/ patterns and
relationships in time series and shape databases. Two concrete
examples are finding the most unusual objects (discord discovery) and
finding repeated objects (motif discovery).
While there are many representations that can be used to solve these
problems (i.e. wavelets, Fourier methods etc), in this talk I argue
that solutions which are scalable to massive datasets will require /
symbolic/ representations. The talk will be illustrated with examples
from anthropology, law enforcement, biology and mining of historical
texts.
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