Engineering comparators for graph clusterings

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

Volumn 5034 LNCS, Issue , 2008, Pages 131-142

  Delling, Daniel ^a   Gaertler, Marco ^a   Görke, Robert ^a   Wagner, Dorothea ^a  

^a UNIVERSITY OF KARLSRUHE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMIC ASPECTS; CLUSTERINGS; EXPERIMENTAL EVALUATIONS; INTERNATIONAL CONFERENCES; NEW APPROACHES; OF GRAPHS; PROMISING APPROACH;

GRAPH THEORY; TECHNOLOGY;

INFORMATION MANAGEMENT;

EID: 48349122541     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-68880-8_14     Document Type: Conference Paper

References (17)

1. Brandes, U., Erlebach, T. (eds.): Network Analysis. LNCS, vol. 3418. Springer, Heidelberg (2005)
2. Brandes, U., Gaertler, M., Wagner, D.: Experiments on Graph Clustering Algorithms. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 568-579. Springer, Heidelberg (2003)
3. Clauset, A., Newman, M., Moore, C.: Finding community structure in very large networks. Physical Review E 70(066111) (2004)
4. Cover, T., Thomas, J.: Elements of Information Theory. John Wiley & Sons, Inc., Chichester (1991)
5. Delling, D., Gaertler, M., Wagner, D.: Generating Significant Graph Clusterings. In: European Conference of Complex Systems (ECCS 2006) (2006)
6. Fred, A., Jain, A.: Robust Data Clustering. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, pp. 128-136 (2003)
7. Gaertler, M., Görke, R., Wagner, D.: Significance-Driven Graph Clustering. In: Kao, M.-Y., Li, X.-Y. (eds.) AAIM 2007. LNCS, vol. 4508, pp. 11-26. Springer, Heidelberg (2007)
8. Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)
9. Meila, M.: Comparing Clusterings by the Variation of Information. In: 16th Annual Conference of Computational Learning Theory (COLT), pp. 173-187 (2003)
10. Meila, M.: Comparing Clusterings - An Axiomatic View. In: 22nd International Conference on Machine Learning, Bonn, Germany, pp. 577-584 (2005)
11. Morey, R., Agresti, A.: The Measurement of Classification Agreement: An Adjustment to the RAND Statistic for Chance Agreement. Educational and Psychological Measurement 44, 33-37 (1984)
12. Rand, W.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66, 846-850 (1971)
13. Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583-617 (2003)
14. van Dongen, S.: A cluster algorithm for graphs. Tech. Report INS-R0010 (2000)
15. van Dongen, S.: Performance criteria for graph clustering and markov cluster experiments. Technical Report INS-R0012, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam (May 2000)
16. Vidal, M.: Interactome modeling. FEBS Lett. 579, 1834-1838 (2005)
17. Wagner, S., Wagner, D.: Comparing Clusterings - An Overview. Technical Report 2006-4, Faculty of Informatics, Universität Karlsruhe (TH) (2006)