Database Reference
In-Depth Information
Algorithmic Learning Theory (ALT2003)
, volume 2842 of
Lecture Notes
in Artificial Intelligence
, pages 175-189. Springer, 2003.
[8] M. Bilenko and R. J. Mooney. Adaptive duplicate detection using learn-
able string similarity measures. In
Proceedings of ACM SIGKDD
, pages
39-48, Washington, DC, 2003.
[9] P. S. Bradley, K. P. Bennett, and A. Demiriz. Constrained K-means clus-
tering. Technical Report MSR-TR-2000-65, Microsoft Research, May
2000.
[10] W. L. Buntine. Operations for learning with graphical models.
Journal
of Artificial Intelligence Research
, 2:159-225, 1994.
[11] H. Chang and D.-Y. Yeung. Locally linear metric adaptation for semi-
supervised clustering. In
Proceedings of 21st International Conference
on Machine Learning (ICML-2004)
, 2004.
[12] D. Cohn, R. Caruana, and A. McCallum. Semi-supervised clustering
with user feedback. Technical Report TR2003-1892, Cornell University,
2003.
[13] T. M. Cover and J. A. Thomas.
Elements of Information Theory
. Wiley-
Interscience, 1991.
[14] I. Davidson, M. Ester, and S. S. Ravi. Ecient incremental clustering
with constraints. In
Proceedings of the Thirteen ACM Conference on
Data Mining and Knowledge Discovery
, 2007.
[15] I. Davidson and S. S. Ravi. Hierarchical clustering with constraints:
Theory and practice. In
Proceedings of the Nineth European Principles
and Practice of KDD (PKDD)
, pages 59-70, 2005.
[16] I. Davidson and S. S. Ravi. Clustering with constraints: Feasibility
issues and the k-means algorithm. In
Proceedings of the 2005 SIAM
International Conference on Data Mining (SDM-05)
, 2005.
[17] A. Demiriz, K. P. Bennett, and M. J. Embrechts. Semi-supervised clus-
tering using genetic algorithms. In
Proceedings of ANNIE
, pages 809-
814, 1999.
[18] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood
from incomplete data via the EM algorithm.
JRSSB
, 39:1-38, 1977.
[19] I. S. Dhillon, J. Fan, and Y. Guan. Ecient clustering of very large
document collections. In
Data Mining for Scientific and Engineering
Applications
. Kluwer Academic Publishers, 2001.
[20] I. S. Dhillon and Y. Guan. Information theoretic clustering of sparse
co-occurrence data. In
Proceedings of ICDM
, pages 517-521, 2003.
Search WWH ::
Custom Search