Seminar on Computational Learning and
Adaptation
Using Background Knowledge to Constrain Clustering
Kiri Wagstaff
Department of Computer Science, Cornell University, and
DaimlerChrysler Research and Technology North America
wkiri@cs.cornell.edu
Clustering algorithms are designed to automatically uncover interesting
structure in data. However, this unsupervised approach can be profitably
augmented by the addition of background information. In this talk, I
will discuss existing methods for using knowledge to constrain clustering
and highlight their limitations. I will also present a new method that
overcomes those limitations, as well as results that demonstrate clear
improvements in learning ability. Finally, I will cover three proposed
application areas that will be used to develop and evaluate other
constraint-capable clustering algorithms.
Some of this material is available, in a written thesis proposal, at
http://www.cs.cornell.edu/home/wkiri/research/wagstaff-proposal.ps
Date: Thurs., June 22
|
Time: 4:15-5:30PM
|
Place: Cordura 100
|
Return to the seminar schedule