Seminar on Computational Learning and
Adaptation
Constrained Clustering for Improved Pattern Discovery
Sepander Kamvar
Department of Scientific Computing/Computational Mathematics
Stanford University
We present an improved method for clustering in the presence of
very limited supervisory information, given as pairwise instance
constraints. By allowing instance-level constraints to have space-level
inductive implications, we are able to successfully incorporate constraints
for a wide range of data set types. Our method greatly improves on the
previously studied COP-K-means algorithm, generally requiring less than half
as many constraints to achieve a given accuracy on a range of real-world
data. We additionally discuss an active learning algorithm which increases
the value of constraints even further.
Date: Thursday, October 3
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Time: 4:15-5:30PM
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Place: Cordura 100
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