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» Approximation algorithms for projective clustering
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KDD
2003
ACM
195views Data Mining» more  KDD 2003»
16 years 1 months ago
Visualizing changes in the structure of data for exploratory feature selection
Using visualization techniques to explore and understand high-dimensional data is an efficient way to combine human intelligence with the immense brute force computation power ava...
Elias Pampalk, Werner Goebl, Gerhard Widmer
91
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ICML
2008
IEEE
16 years 1 months ago
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
107
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ECCV
2002
Springer
16 years 2 months ago
On Affine Invariant Clustering and Automatic Cast Listing in Movies
Abstract We develop a distance metric for clustering and classification algorithms which is invariant to affine transformations and includes priors on the transformation parameters...
Andrew W. Fitzgibbon, Andrew Zisserman
87
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ICASSP
2009
IEEE
15 years 7 months ago
Exploring functional connectivity in fMRI via clustering
In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering...
Archana Venkataraman, Koene R. A. Van Dijk, Randy ...
97
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KAIS
2006
126views more  KAIS 2006»
15 years 18 days ago
Fast and exact out-of-core and distributed k-means clustering
Clustering has been one of the most widely studied topics in data mining and k-means clustering has been one of the popular clustering algorithms. K-means requires several passes ...
Ruoming Jin, Anjan Goswami, Gagan Agrawal