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ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
13 years 11 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
SDM
2007
SIAM
86views Data Mining» more  SDM 2007»
13 years 6 months ago
Identifying Bundles of Product Options using Mutual Information Clustering
Mass-produced goods tend to be highly standardized in order to maximize manufacturing efficiencies. Some high-value goods with limited production quantities remain much less stand...
Claudia Perlich, Saharon Rosset
CIVR
2007
Springer
192views Image Analysis» more  CIVR 2007»
13 years 11 months ago
Texture retrieval based on a non-parametric measure for multivariate distributions
In the present study, an efficient strategy for retrieving texture images from large texture databases is introduced and studied within a distributional-statistical framework. Our...
Vasileios K. Pothos, Christos Theoharatos, George ...
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...
ICASSP
2011
IEEE
12 years 8 months ago
An utterance comparison model for speaker clustering using factor analysis
We propose a novel utterance comparison model based on probability theory and factor analysis that computes the likelihood of two speech utterances originating from the same speak...
Woojay Jeon, Changxue Ma, Dusan Macho