Kernel k-means clustering applied to vector space embeddings of graphs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5064 LNAI, Issue , 2008, Pages 24-35

  Riesen, Kaspar ^a   Bunke, Horst ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; FEATURE EXTRACTION; FLOW OF SOLIDS; GRAPH THEORY; NETWORK PROTOCOLS; PATTERN RECOGNITION; SENSOR NETWORKS; SUPPORT VECTOR MACHINES; TECHNICAL PRESENTATIONS; VECTORS; WATER SUPPLY SYSTEMS;

BASIC IDEAS; CLUSTERING; CLUSTERING PROCEDURES; EDIT DISTANCES; EMBEDDED GRAPHS; EMBEDDING PROCEDURES; EMBEDDINGS; GRAPH CLUSTERING; GRAPH DATUM; INPUT GRAPHS; K-MEANS CLUSTERING; OF GRAPHS; REAL VECTOR SPACES; VECTOR SPACES;

NEURAL NETWORKS;

EID: 54249145109     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-69939-2_3     Document Type: Conference Paper

References (22)

1. Jain, A., Murty, M., Flynn, P.: Data clustering: A review. ACM Computing Surveys 31(3), 264-323 (1999)
2. Englert, R., Glantz, R.: Towards the clustering of graphs. In: Kropatsch, W., Jolion, J. (eds.) Proc. 2nd Int. Workshop on Graph Based Representations in Pattern Recognition, pp. 125-133 (2000)
3. Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. Journal of Pattern Recognition and Artificial Intelligence 18(3), 265-298 (2004)
4. Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
5. Gärtner, T.: A survey of kernels for structured data,. SIGKDD Explorations 5(1), 49-58 (2003)
6. Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recognition Letters 1, 245-253 (1983)
7. Riesen, K., Neuhaus, M., Bunke, H.: Bipartite graph matching for computing the edit distance of graphs. In: Escolano, F., Vento, M. (eds.) GbRPR. LNCS, vol. 4538, pp. 1-12. Springer, Heidelberg (2007)
8. Duin, R., Pekalska, E.: The Dissimilarity Representations for Pattern Recognition: Foundations and Applications. World Scientific, Singapore (2005)
9. Riesen, K., Neuhaus, M., Bunke, H.: Graph embedding in vector spaces by means of prototype selection. In: Escolano, F., Vento, M. (eds.) GbRPR. LNCS, vol. 4538, pp. 383-393. Springer, Heidelberg (2007)
10. MacQueen, J.: Some methods for classification and analysis of multivariant observations. In: Proc. 5th. Berkeley Symp., vol. 1, pp. 281-297. University of California Press 1 (1966)
11. Dunn, J.: Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics 4, 95-104 (1974)
12. Hubert, L., Schultz, J.: Quadratic assignment as a general data analysis strategy. British Journal of Mathematical and Statistical Psychology 29, 190-241 (1976)
13. Rand, W.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846-850 (1971)
14. Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics 5, 32-38 (1957)
15. Nene, S., Nayar, S., Murase, H.: Columbia Object Image Library: COIL-100. Technical report, Department of Computer Science, Columbia University, New York (1996)
16. Watson, C., Wilson, C.: NIST Special Database 4, Fingerprint Database. National Institute of Standards and Technology (1992)
17. Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shidyalov, I., Bourne, P.: The protein data, bank. Nucleic Acids Research 28, 235-242 (2000)
18. DTP, DTP.: AIDS antiviral screen (2004),
19. http://dtp.nci.nih.gov/docs/aids/aids_data.html
20. Schenker, A., Bunke, H., Last, M., Kandel, A.: Graph-Theoretic Techniques for Web Content Mining. World Scientific, Singapore (2005)
21. Cox, T., Cox, M.: Multidimensional Scaling. Chapman and Hall, Boca Raton (1994)
22. Kuncheva, L., Vetrov, D.: Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1798-1808 (2006)