Benchmarking graph-based clustering algorithms

Image and Vision Computing

Volumn 27, Issue 7, 2009, Pages 979-988

  Foggia, P ^a   Percannella, G ^b   Sansone, C ^a   Vento, M ^b  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b UNIVERSITY OF SALERNO   (Italy)

Author keywords

Benchmarking; Cluster detection; Graph based clustering

Indexed keywords

BENCHMARKING; CLUSTER ANALYSIS; LEARNING SYSTEMS;

CLUSTER DETECTION; CLUSTERING APPROACHES; COMPARATIVE ANALYSIS; DATA-SETS; EFFECTIVE SOLUTIONS; GAUSSIAN; GRAPH-BASED; GRAPH-BASED CLUSTERING; GRAPH-BASED METHODS; MINIMUM SPANNING TREES; NUMBER OF CLUSTERS; PERFORMANCE EVALUATIONS; REAL APPLICATIONS; SIZE AND SHAPES; SPHERICAL DISTRIBUTIONS; UCI MACHINE LEARNING REPOSITORIES; UNSUPERVISED METHODS;

CLUSTERING ALGORITHMS;

EID: 64849083317     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2008.05.002     Document Type: Article

References (28)

1. Jain A.K., Murty M.N., and Flynn P.J. Data clustering: a review. ACM Computing Surveys 31 3 (1999) 264-323
2. Jain A.K., and Dubes R.C. Algorithms for Clustering Data (1988), Prentice-Hall, Inc., Upper Saddle River, NJ, USA
3. Kohonen T. Self-Organizing Maps (1995), Springer-Verlag, Heidelberg, Germany
4. P. Juszczak, Learning to recognise, A study on one-class classification and active learning, PhD thesis, Delft University of Technology, ISBN: 978-90-9020684-4, 2006.
5. Kima D.W., Leeb K.Y., Leea D., and Leea K.H. Evaluation of the performance of clustering algorithms in kernel-induced feature space. Pattern Recognition 38 (2005) 607-611
6. Cheng H.D., Cai X., Chen X., Hu L., and Lou X. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36 (2003) 2967-2991
7. Wu Z., and Leahy R. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15 11 (1993) 1101-1113
8. K.-H.Anders, M. Sester, Parameter-free cluster detection in spatial databases and its application to typification, in: Proceedings of IAPRS Congress, Amsterdam, vol. XXXIII, Part B4/1, Comm. IV, 2000, pp. 75-82.
9. De Santo M., Percannella G., Sansone C., and Vento M. Combining experts for anchorperson shot detection in news videos. Pattern Analysis and Applications 7 4 (2005) 447-460
10. U. Brandes, M. Gaertler, D. Wagner, Experiments on graph clustering algorithms, in: Proceedings of the 11th European Symposium on Algorithms (ESA '03), LNCS 2832, Springer-Verlag, 2003, pp. 568-579.
11. Foggia P., Percannella G., Sansone C., and Vento M. Assessing the performance of a graph-based clustering algorithm. In: Escolano F., and Vento M. (Eds). Lecture Notes in Computer Science vol. 4538 (2007), Springer-Verlag, Berlin pp. 215-227
12. De Santo M., Foggia P., Sansone C., and Vento M. A large database of graphs and its use for benchmarking graph isomorphism algorithms. Pattern Recognition Letters 24 8 (2003) 1067-1079
13. Günter S., and Bunke H. Validation indices for graph clustering. Pattern Recognition Letters 24 8 (2003) 1107-1113
14. Malik U., and Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 12 (2002) 1650-1654
15. A. Asuncion, D.J. Newman, UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html], University of California, Irvine, CA, 2007.
16. S.M. van Dongen, Graph clustering by flow simulation, PhD thesis, University of Utrecht, 2000.
17. R. Kannan, S. Vampala, A. Vetta, On clustering: good, bad and spectral, Foundations of Computer Science (2000) 367-378.
18. M. Gaertler, Clustering with spectral methods, Master's thesis, Universitat Konstanz, 2002.
19. Shi J., and Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 (2000) 888-905
20. Zahn C. Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers C-20 (1971) 68-86
21. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
22. Horowitz E., and Sahni S. Fundamentals of Computer Algorithms (1978), Computer Science Press
23. Enright A.J., van Dongen S., and Ouzounis C.A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research 30 7 (2002) 1575-1584
24. Davies D.L., and Bouldin D.W. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1 (1979) 224-227
25. Dunn C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3 (1973) 32-57
26. Calinski R.B., and Harabasz J. A dendrite method for cluster analysis. Communications in Statistics 3 (1974) 1-27
27. Xie X.L., and Beni G. A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13 (1991) 841-847
28. Hubert L., and Schultz J. Quadratic assignment as a general data-analysis strategy. British Journal of Mathematical and Statistical Psychology 29 (1976) 190-241