Grassmann Clustering
Grassmann Clustering
Grassmann Clustering
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n(t)<br />
s(t) �<br />
A<br />
F. Theis<br />
x(t)<br />
I. Introduction<br />
<strong>Clustering</strong><br />
goal:<br />
• given a multivariate data set A<br />
• determine<br />
• partition into groups (clusters)<br />
• representative cluster centers (centroids)<br />
approaches:<br />
• partitional clustering (here)<br />
• hierarchical clustering<br />
partitional<br />
clustering<br />
5<br />
hierarchical<br />
Apr 6, 2006 :: Tübingen