Local equivalences of distances between clusterings - A geometric perspective

Machine Learning

Volumn 86, Issue 3, 2012, Pages 369-389

  Meilǎ, Marina ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

2 divergence; Clustering; Comparing partitions; Convexity; Misclassification error; Rand index

Indexed keywords

CLUSTER VALIDATION; CLUSTERING; CLUSTERINGS; CONVEXITY; DATA CLUSTERING; DETERMINING THE NUMBER OF CLUSTERS; MISCLASSIFICATION ERROR; PROBABILITY THEORY; RAND INDEX; THEORETICAL STUDY; UPPER AND LOWER BOUNDS;

GEOMETRY; HAMMING DISTANCE;

CLUSTERING ALGORITHMS;

EID: 84868302138     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-011-5267-2     Document Type: Article

References (18)

1. Bach, F., & Jordan, M. I. (2006). Learning spectral clustering with applications to speech separation. Journal of Machine Learning Research, 7, 1963-2001. (Pubitemid 44497457)
2. Ben-David, S., von Luxburg, U., & Pal, D. (2006). A sober look at clustering stability. In 19th annual conference on learning theory, COLT 2006. Berlin: Springer.
3. Candès, E. J., & Tao, T. (2005). The Dantzig selector: statistical estimation when p is much larger than n. Annals of Statistics, 35, 2313-2351.
4. Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.
5. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: Wiley.
6. Donoho, D. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289-1306.
7. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193-218.
8. Lancaster, H. (1969). The Chi-squared distribution. New York: Wiley.
9. Meilǎ, M. (2005). Comparing clusterings - an axiomatic view. In S. Wrobel & L. De Raedt (Eds.), Proceedings of the international machine learning conference (ICML). New York: ACM Press.
10. Meilǎ, M. (2006). The uniqueness of a good optimum for K-means. In A. Moore &W. Cohen (Eds.), Proceedings of the international machine learning conference (ICML) (pp. 625-632). Princeton: International Machine Learning Society.
11. Meilǎ, M. (2007). Comparing clusterings - an information based distance. Journal of Multivariate Analysis, 98(5), 873-895.
12. Meilǎ, M., Shortreed, S., & Xu, L. (2005). Regularized spectral learning. In R. Cowell & Z. Ghahramani (Eds.), Proceedings of the artificial intelligence and statistics workshop (AISTATS 05).
13. Mirkin, B. G. (1996). Mathematical classification and clustering. Dordrecht: Kluwer Academic.
14. Papadimitriou, C., & Steiglitz, K. (1998). Combinatorial optimization. Algorithms and complexity.Minneola: Dover.
15. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66, 846-850.
16. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels. Cambridge: MIT Press.
17. Srebro, N., Shakhnarovich, G., & Roweis, S. (2006). An investigation of computational and informational limits in Gaussian mixture clustering. In Proceedings of the 23rd international conference on machine learning (ICML).
18. Vajda, I. (1989). Theory of statistical inference and information. Theory and decision library. Series B: Mathematical and statistical methods. Norwell: Kluwer Academic Publishers.