Supervised evaluation of Voronoi partitions

Intelligent Data Analysis

Volumn 10, Issue 3, 2006, Pages 269-283

  Ferrandiz, Sylvain ^a,b   Boullé, Marc ^a  

^a ORANGE LABS   (France)

^b UNIVERSITÉ DE CAEN   (France)

Author keywords

data dependent evaluation; nearest neighbor rule; partitioning; Supervised classification; Voronoi tesselations

Indexed keywords

DATA DEPENDENT; NEAREST NEIGHBOR RULE; PARTITIONING; SUPERVISED CLASSIFICATION; VORONOI;

EID: 78049526646     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2006-10306     Document Type: Article

References (16)

1. C.L. Blake and C.J. Merz, UCI repository of machine learning databases, http://www.ics.uci.edu/mlearn/MLRepository. html.
2. C. Eick, N. Zeidat and R.Vilalta, Using representative-based clustering for nearest neighbor dataset editing, Proceedings of the 4th International Conference on Data Mining, 2004, 375-378.
3. E. Fix and J. Hodges, Discriminatory analysis, Nonparametric discrimination: consistency properties, Technical Report 4, USAF School of Aviation Medicine, Randolf Field, TX, 1951.
4. T. Hastie, R. Tibshirani and J. Friedman, The elements of statistical learning, Springer, 2001.
5. L. Kaufman and P.J. Rousseeuw, in: Clustering by means of medoids, Statistical Data Analysis Based on the L1-Norm, Y. Dodge, ed., Amsterdam, The Netherlands: North-Holland, 1987, pp. 405-416.
6. L. Kaufman and P.J. Rousseeuw, Finding groups in data: an introduction to cluster analysis, Wiley & Sons, 1990.
7. T. Kohonen, Self-organizing maps, Springer, 3rd edition, 2001.
8. P. Kontkanen, P. Myllym̈aki, W. Buntime, J. Rissanen and H. Tirri, An MDL framework for data clustering, Advances in Minimum Description Length: Theory and Application, Grunwald, Myung and Pitt, MIT Press, 2005.
9. J. McQueen, Some methods for classification and analysis of multivariate observations, Fifth Berkeley Symposium on Mathematical Statistics and Probability, Le Cam and Neyman 1 (1967), 281-297.
10. R.T.Ng and J. Han, CLARANS: a method for clustering objects for spatial data mining, IEEE Transactions on Knowledge and Data Engineering 14-5 (2002), 1003-1016.
11. F.P. Preparata and M.I. Shamos, Computational geometry: an introduction, Springer, 1986.
12. J. Rissanen, Fisher information and stochastic complexity, IEEE Transactions on Information Theory 42-1 (1996), 40-47.
13. C.E. Shannon, A mathematical theory of communication, Bell Systems Technical Journal 27 (1948), 379-423 and 623-656.
14. J. Sinkkonen, S. Kaski and J. Nikkil̈a, Discriminative Clustering: optimal contingency tables by learning metrics, Proceedings of the 13th European Conference on Machine Learning, 2002, 418-430.
15. N. Slonim and N. Tishby, Agglomerative information bottleneck, Proceedings of Neural Information Processing System 12 (2000), 617-623.
16. D.R. Wilson and T.R. Martinez, Reduction techniques for instance-based learning algorithms, Machine Learning 38-3 (2000), 257-286.