In order to find a connection between the works studied (Bregman Co-clustering and Support Vector Clustering) we have performed some research. An interesting result are the following paper:

• bibtex | link
  R. Nock and F. Nielsen, "Fitting the smallest enclosing Bregman balls," in 16th European Conference on Machine Learning, 2005, pp. 649-656.
  @conference{bregmanmeb05,
  author = {Richard Nock and Frank Nielsen},
  Booktitle = {16th European Conference on Machine Learning},
  Date-Added = {2007-06-23 11:00:19 +0200},
  Date-Modified = {2007-11-14 12:55:32 +0100},
  Keywords = {bregman, MEB},
  Number = {3720},
  Pages = {649–656},
  Publisher = {Springer-Verlag},
  Series = {Lectures Notes on Computer Science Series},
  Title = {{Fitting the smallest enclosing Bregman balls}},
  Url = {http://www.sonycsl.co.jp/person/nielsen/BregmanBall/nn-ecml-05.pdf},
  Year = {2005},
  Bdsk-File-1 = {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},
  Bdsk-Url-1 = {http://www.sonycsl.co.jp/person/nielsen/BregmanBall/nn-ecml-05.pdf}
}

The above paper generalizes the Minimum Enclosing Ball (MEB) problem to the Bregman divergences and also provide a generalization of the Bâdoiu-Clarkson (BC) approximation algorith. This is the same algorithm exploited in practical by the Core Vector Machines

• bibtex | link
  I. W. Tsang, J. T. Kwok, and P. Cheung, "Core vector machines: Fast SVM training on very large data sets," Journal of Machine Learning Research, vol. 6, pp. 363-392, 2005.
  @article{cvm05,
  author = {Ivor W. Tsang and James T. Kwok and Pak-Ming Cheung},
  Date-Added = {2007-05-26 12:49:30 +0200},
  Date-Modified = {2007-06-23 08:23:02 +0200},
  Journal = {Journal of Machine Learning Research},
  Keywords = {SVM, CVM, MEB, SVDD},
  Pages = {363–392},
  Title = {Core vector machines: Fast SVM training on very large data sets},
  Url = {http://www.cs.ust.hk/%7Eivor/publication/tsang05a.pdf},
  Volume = {6},
  Year = {2005},
  Bdsk-File-1 = {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},
  Bdsk-Url-1 = {http://www.cs.ust.hk/~ivor/publication/tsang05a.pdf}
}

CVMs reformulate the SVMs as a MEB problem. Since they use the BC algorithm and such an algorithm has been generalized to the Bregman divergences, the research on vector machines could have interesting implications.