Affinity propagation clustering with geodesic distances

Journal of Computational Information Systems

Volumn 6, Issue 1, 2010, Pages 47-53

  Zhang, Liang ^a   Du, Ziping ^a  

^a TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Affinity propagation; Geodesic distance; Kernel method

Indexed keywords

AFFINITY PROPAGATION; CLUSTERING METHODS; DATA POINTS; DATA SETS; FEATURE SPACE; GEODESIC DISTANCES; INHERENT STRUCTURES; KERNEL METHODS; MAPPING DATA; REAL-WORLD DATASETS;

CLUSTERING ALGORITHMS; GEODESY;

DATA FLOW ANALYSIS;

EID: 77954332208     PISSN: 15539105     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. Sokal, R. R. and Michener, C. D., Statistical method for evaluating systematic relationships, University of Kansas science bulletin, 38:1409-1438, 1958
2. MacQueen J. B., Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1:281-297, 1967
3. Filippone M., Camastra F., Masulli F. and Rovetta S., A survey of kernel and spectral methods for clustering, Pattern Recognition, v.41 n.1, p.176-190, January, 2008
4. Banerjee A., Dhillon I. S., Ghosh J. and Sra S., Clustering on the unit hypersphere using von Mises-Fisher distributions, Journal of Machine Learning Research, 6: 1345-1382, 2005
5. Raftery A.E. and Dean N., Variable Selection for Model-Based Clustering, Journal of the American Statistical Association, Vol. 101, No. 473: 168-178, 2006
6. Handcock, M. S., Raftery A. E. and Tantrum J., Model-based clustering for social Networks, Journal of the Royal Statistical Society A. 170(2):301-354, 2007
7. Ding C. and He X., K-means clustering via principal component analysis. In Proceedings of the 24th International Conference on Machine learning (ICML04), pages 225-232, 2004
8. Roweis S. and Saul L., Nonlinear dimensionality reduction by locally linear embedding, In Science, 2000
9. Belkin M. and Niyogi P., Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6), 2003
10. Tenenbaum J. B., de Silva V. and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2000
11. Yang L., http://www.cs.cmu.edu/~liuy/dist_overview.pdf
12. Frey B. J. and Dueck D., Clustering by passing messages between data points. Science 315, 972-976, 2007
13. Wu A.Y., Garland M. and Han J., Mining scale-free networks using geodesic clustering, KDD'04, August 22-25, 2004, Seattle, Washington, USA
14. Feil B. and Abonyi J., Geodesic distance based fuzzy clustering, Advances in Soft Computing, 39:50-59, 2007
15. Kim J., Shim K. and Choi S.. Soft geodesic kernel k-means. In IEEE International Conference on Acoustics, Speech and Signal, volume 2, pages 429-432, 2007
16. http://www.ics.uci.edu/~mlearn/MLRepository.html
17. LeCun Y., Boser B., Denker J. S., Henderson D., Howard R. E., Hubbard W. and Jackel L. J., Backpropagation applied to handwritten zip code recognition. Neural Computation, 1:541-551, 1989