Clustering and classification based on the L1 data depth

Journal of Multivariate Analysis

Volumn 90, Issue 1 SPEC. ISS., 2004, Pages 67-89

  Jörnsten, Rebecka ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

Classification; Clustering; Data depth; Relative data depth

Indexed keywords

EID: 19744375379     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1016/j.jmva.2004.02.013     Document Type: Article

References (15)

1. U. Alon, N. Barkai, D.A. Notterdam, K. Gish, S. Ybarra, D. Mack, A.J. Levine, Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proc. Nat. Acad. Sci. 96 (1999) 6745-6750.
2. A. Christmann, Classification based on the SVM and on regression depth, statistical data analysis based on the L1norm and related methods, Statistics for Industry and Technology, Birkhauser, Basel, 2002.
3. S. Dudoit, J. Fridlyand, Application of resampling methods to estimate the number of clusters and to improve the accuracy of a clustering method, Technical Report 600, Department of Statistics, UC Berkeley, 2001.
4. S. Dudoit, J. Fridlyand, T. Speed, Comparison of discrimination methods for the classification of tumours using gene expression data, J. Amer. Statist. Assoc. 97 (2002) 77-87.
5. Silicon Genetics. Genespring. http://www.sigenetics.com/Products/ GeneSpring/,2002.
6. R. Gentleman, R. Ihaka, Their project, http://www.r-project.org/.
7. T.R. Gloub, D.K. Slonim, P. Tamayo, C. Huard, M. Gassenbeek, J.P. Mesirov, H. Coller, M.L. Loh, J.R. Downing, M. A. Caliguiri, C.D. Bloomfield, E.S. Lander, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science 286 (1999) 531-537.
8. T. Hastie, R. Tibshirani, D. Botstein, P. Brown, Supervised harvesting of expression trees, Technical Report, Department of Statistics, Stanford University, 2000.
9. R. Jörnsten, Y. Vardi, C.-H. Zhang, A robust clustering method and visualization tool based on data depth, statistical data analysis based on the L1norm and related methods, Statistics for Industry and Technology, Birkhauser, Basel, 2002.
10. L.Kaufman, P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, 1990.
11. R. Liu, P. Parelius, K. Singh, Multivariate analysis by data depth: descriptive statistics, graphics and inference (with discussion), Ann. Statist. 27 (1999) 783-858.
12. G.J. Sullivan, Efficient scalar quantization of exponential and laplacian random variables, IEEE Trans. Inform. Theory 42 (1996) 1365-1374.
13. R. Tibshirani, G. Walther, D. Botstein, P. Brown, Cluster validation by prediction strength, Technical Report, Department of Biostatistics, Stanford University, 2001.
14. M. van der Laan, K. Pollard, J. Bryan, A new partitioning around medoids algorithm, Technical Report, Division of Biostatistics, UC Berkeley, 2002.
15. 1-median and associated data depth, Proc. Nat. Acad. Sci. 97 (2000) 1423-1426.