Database Reference
In-Depth Information
Song, L., Smola, A. J., Gretton, A., & Borgwardt,
K. M. (2007). A Dependence Maximization View
of Clustering. In
Proc. of International Conference
on Machine Learning (ICML)
(pp.815-822).
Weber, R., Schek, H.-J., & Blott, S. (1998). A
Quantative Analysis and Performance Study for
Similarity-search Methods in High-dimensional
Spaces. In
Proc. of International Conference on
Very Large Data Bases (VLDB),
(pp. 194-205).
Stoer, M., & Wagner, F. (1997). A Simple Min-cut
Algorithm.
Journal of the ACM
,
44
(4), 585-591.
doi:10.1145/263867.263872
Xing, E., Ng,A., Jordan, M., & Russell, S. (2003).
Distance Metric Learning, with Application to
Clustering with Side-information. In
Proc. of
15th
Annual Conference on Advances in Neural
Information Processing Systems (NIPS),
(pp.
505-512).
Tishby, N., Pereira, F. C., & Bialek, W. (2000).
The Information Bottleneck Method. In
CoR-
Rphysics/0004057.
Tishby, N., & Slonim, N. (2000). Data Clustering
by Markovian Relaxation and the Information
Bottleneck Method.
Proc. of 13
th
Annual Confer-
ence on Advances in Neural Information Process-
ing Systems (NIPS),
(pp. 640-646).
Yip, K. Y., Cheung, D. W., & Ng, M. K. (2005).
On Discovery of Extremely Low-Dimensional
Clusters using Semi-Supervised Projected Cluster-
ing. In
IEEE International Conference on Data
Engineering (ICDE),
(pp. 329-340).
Tsuda, K., & Kudo, T. (2006). Clustering Graphs
by Weighted Substructure Mining. In
Proc. of
International Conference on Machine Learning
(ICML),
(pp. 953-960).
Zaiane, O. R., Man, X., & Han, J. (1998). Dis-
covering Web Access Patterns and Trends by
Applying OLAP and Data Mining Technology
on Web Logs. In
IEEE Forum on Research and
Technology Advances in Digital Libraries (ADL),
(pp. 19-29).
Wagner, D., & Wagner, F. (1993). Between Min-
cut and Graph Bisection. In
Proc. of International
Symposium on Mathematical Foundations of
Computer Science (MFCS),
(pp. 744-750).
Zelnik-Manor, L., & Perona, P. (2004). Self-
Tuning Spectral Clustering. In
Proc. of 17
th
Annual
Conference on Advances in Neural Information
Processing Systems (NIPS),
(pp. 1601-1608).
Wagstaff, K., Cardie, C., Rogers, S., & Schroedel,
S. (2001). Constrained K-means Clustering with
Background Knowledge. In
Proc. of Interna-
tional Conference on Machine Learning (ICML),
(pp.577-584).
Zeng, E., Chengyong, Y., Tao, L., & Narasimhan,
G. (2007). On the Effectiveness of Constraints
Sets in Clustering Genes. In
IEEE International
Conference on Bioinformatics and Bioengineering
(BIBE),
(pp. 79-86).
Search WWH ::
Custom Search