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NIPS
2003
10 years 2 months ago
An Iterative Improvement Procedure for Hierarchical Clustering
We describe a procedure which finds a hierarchical clustering by hillclimbing. The cost function we use is a hierarchical extension of the  -means cost; our local moves are tree...
David Kauchak, Sanjoy Dasgupta
ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
10 years 8 months ago
Worst-Case and Smoothed Analysis of k-Means Clustering with Bregman Divergences
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
Bodo Manthey, Heiko Röglin
ECCV
2002
Springer
11 years 3 months ago
Another Way of Looking at Plane-Based Calibration: The Centre Circle Constraint
Abstract. The plane-based calibration consists in recovering the internal parameters of the camera from the views of a planar pattern with a known geometric structure. The existing...
Alain Crouzil, Pierre Gurdjos, René Payriss...
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