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» Quantization and clustering with Bregman divergences
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KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 5 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
ICASSP
2010
IEEE
13 years 5 months ago
Hierarchical Gaussian Mixture Model
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image ...
Vincent Garcia, Frank Nielsen, Richard Nock
ALT
2009
Springer
14 years 1 months ago
Approximation Algorithms for Tensor Clustering
Abstract. We present the first (to our knowledge) approximation algorithm for tensor clustering—a powerful generalization to basic 1D clustering. Tensors are increasingly common...
Stefanie Jegelka, Suvrit Sra, Arindam Banerjee
PAMI
2006
128views more  PAMI 2006»
13 years 4 months ago
On Weighting Clustering
Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via weights on the instance po...
Richard Nock, Frank Nielsen
TALG
2010
158views more  TALG 2010»
12 years 11 months ago
Clustering for metric and nonmetric distance measures
We study a generalization of the k-median problem with respect to an arbitrary dissimilarity measure D. Given a finite set P of size n, our goal is to find a set C of size k such t...
Marcel R. Ackermann, Johannes Blömer, Christi...