The uniqueness of a good optimum for K-means

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 625-632

  Meilǎ, Marina ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PROBLEM SOLVING; SET THEORY; SPECTRUM ANALYSIS;

DATA SETS; DISTORTION CLUSTERING; SPECTRAL BOUNDS;

DATA REDUCTION;

EID: 33749236636     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Ben-Hur, A., Elisseeff, A., & Guyon, I. (2002). A stability based method for discovering structure in clustered data. Pacific Symposium on Biocomputing (pp. 6-17).
2. Dasgupta, S. (1999). Learning mixtures of gaussians. FOCS '99: Proceedings of the 40th Annual Symposium on Foundations of Computer Science (p. 634). Washington, DC, USA: IEEE Computer Society.
3. Ding, C., & He, X. (2004). K-means clustering via principal component analysis. Proceedings of the International Machine Learning Conference (ICML). Morgan Kauffman.
4. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193-218.
5. Lancaster, H. (1969). The Chi-squared distribution. Wiley.
6. Lange, T., Roth, V., Braun, M. L., & Buhmann, J. M. (2004). Stability-based validation of clustering solutions. Neural Comput., 16, 1299-1323.
7. Meilǎ, M. (2005). Comparing clusterings - an axiomatic view. Proceedings of the International Machine Learning Conference (ICML). ACM Press.
8. 2 distance, (submitted).
9. Meilǎ, M., Shortreed, S., & Xu, L. (2005). Regularized spectral learning. Proceedings of the Artificial Intelligence and Statistics Workshop(AISTATS 05).
10. Papadimitriou, C., & Steiglitz, K. (1998). Combinatorial optimization, algorithms and complexity. Minneola, NY: Dover Publication, Inc.
11. Vempala, S., & Wang, G. (2004). A spectral algorithm for learning mixture models. J. Comput. Syst. Sci., 68, 841-860.