Shan, H. & Banerjee, A.
(2008):
Bayesian Co-clustering..
In: ICDM,
[Volltext]
[BibTeX][Endnote]
@inproceedings{conf/icdm/ShanB08,
author = {Shan, Hanhuai and Banerjee, Arindam},
title = {Bayesian Co-clustering.},
booktitle = {ICDM},
publisher = {IEEE Computer Society},
year = {2008},
pages = {530-539},
url = {http://dblp.uni-trier.de/db/conf/icdm/icdm2008.html#ShanB08},
keywords = {co-clustering, bayesian, clustering, lda}
}
%0 = inproceedings
%A = Shan, Hanhuai and Banerjee, Arindam
%B = ICDM
%D = 2008
%I = IEEE Computer Society
%T = Bayesian Co-clustering.
%U = http://dblp.uni-trier.de/db/conf/icdm/icdm2008.html#ShanB08
Banerjee, A.; Merugu, S.; Dhillon, I. S. & Ghosh, J.
(2005):
Clustering with Bregman Divergences..
In: Journal of Machine Learning Research,
Vol. 6,
Erscheinungsjahr/Year: 2005.
Seiten/Pages: 1705-1749.
[Volltext] [BibTeX]
[Endnote]
@article{journals/jmlr/BanerjeeMDG05,
author = {Banerjee, Arindam and Merugu, Srujana and Dhillon, Inderjit S. and Ghosh, Joydeep},
title = {Clustering with Bregman Divergences.},
journal = {Journal of Machine Learning Research},
year = {2005},
volume = {6},
pages = {1705-1749},
url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlr6.html#BanerjeeMDG05},
keywords = {mixture, bayesian, clustering, lda, models}
}
%0 = article
%A = Banerjee, Arindam and Merugu, Srujana and Dhillon, Inderjit S. and Ghosh, Joydeep
%D = 2005
%T = Clustering with Bregman Divergences.
%U = http://dblp.uni-trier.de/db/journals/jmlr/jmlr6.html#BanerjeeMDG05
Basu, S.; Banerjee, A. & Mooney, R. J.
(2004):
Active Semi-Supervision for Pairwise Constrained Clustering.
Erscheinungsjahr/Year: 2004.
Seiten/Pages: 333-344.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
Semi-supervised clustering uses a small amount of supervised
ta to aid unsupervised learning. One typical approach
ecifies a limited number of must-link and cannotlink
nstraints between pairs of examples. This paper
esents a pairwise constrained clustering framework and a
w method for actively selecting informative pairwise constraints
get improved clustering performance. The clustering
d active learning methods are both easily scalable
large datasets, and can handle very high dimensional data.
perimental and theoretical results confirm that this active
erying of pairwise constraints significantly improves the
curacy of clustering when given a relatively small amount
supervision.
@article{Basu:EtAl:04,
author = {Basu, Sugato and Banerjee, Arindam and Mooney, Raymond J.},
title = {Active Semi-Supervision for Pairwise Constrained Clustering},
booktitle = {Proceedings of the SIAM International Conference on Data Mining},
address = {Lake Buena Vista, FL},
year = {2004},
pages = {333--344},
url = {http://www.cs.utexas.edu/users/ml/papers/semi-sdm-04.pdf},
keywords = {clustering, semi, active, supervised},
abstract = {Semi-supervised clustering uses a small amount of supervised
data to aid unsupervised learning. One typical approach
specifies a limited number of must-link and cannotlink
constraints between pairs of examples. This paper
presents a pairwise constrained clustering framework and a
new method for actively selecting informative pairwise constraints
to get improved clustering performance. The clustering
and active learning methods are both easily scalable
to large datasets, and can handle very high dimensional data.
Experimental and theoretical results confirm that this active
querying of pairwise constraints significantly improves the
accuracy of clustering when given a relatively small amount
of supervision.}
}
%0 = article
%A = Basu, Sugato and Banerjee, Arindam and Mooney, Raymond J.
%B = Proceedings of the SIAM International Conference on Data Mining
%C = Lake Buena Vista, FL
%D = 2004
%T = Active Semi-Supervision for Pairwise Constrained Clustering
%U = http://www.cs.utexas.edu/users/ml/papers/semi-sdm-04.pdf