Clustering methods for risk assessment of infrastructure network systems

Applications of Statistics and Probability in Civil Engineering -Proceedings of the 11th International Conference on Applications of Statistics and Probability in Civil Engineering

Volumn , Issue , 2011, Pages 1389-1397

  Gomez C ^a   Sanchez Silva M ^a   Duenas Osorio L ^b  

^a UNIVERSIDAD DE LOS ANDES   (Colombia)

^b Rice University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING METHODS; COMPLEX NETWORKS; COMPUTATIONAL TECHNIQUE; HIERARCHICAL REPRESENTATION; ILLUSTRATIVE EXAMPLES; INFRASTRUCTURE NETWORKS; INFRASTRUCTURE SYSTEMS; NETWORK COMPLEXITY; NETWORK-BASED; NP-HARD; NUMBER OF STATE; RECURSIVE DECOMPOSITION; RELIABILITY ASSESSMENTS; RELIABILITY ESTIMATION; RELIABLE OPERATION; SOCIO-ECONOMIC DEVELOPMENT;

CIVIL ENGINEERING; NETWORK PERFORMANCE; PROBABILITY; RELIABILITY ANALYSIS;

HIERARCHICAL SYSTEMS;

EID: 84856704211     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Blockley D.I. & Godfrey P. (2000), Do it differently: systems for rethinking construction. Thomas Telford, London.
2. Camastra, F. & Verri, A. (2005), A Novel Kernel Method for Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 27, Issue 5 Pages: 801-804.
3. Chan, P., Schlag, M. & Zien, J. (1994), Spectral k-way ratio cut partitioning, IEEE Trans. CAD-Integrated Circuits and Systems, vol. 13, pp. 1088-1096.
4. Checkland P.B. (1981), Systems thinking, systems practice. John Wiley and Sons, Chichester, UK.
5. Choi, M.S. & Jun, C.H. (1995), Some Variant of Polygon- Chain- Reductions in Evaluating Reliability of Undirected Network. Microelectron. Reliab., 35(1), 1-11.
6. Davies, D.L. & Bouldin D.W. (1979), A Cluster Separation Measure, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 1, pp. 224-227.
7. Dhillon I.S., Guan Y. & Kulis B. (2005), A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts, UTCS Technical Report No. TR-04-25.
8. Duenas-Osorio L. & Rojo J. (2010), Reliability assessment of lifeline systems with radial topology. Computer-aided Civil and Infrastructure Engineering DOI: 10.1111/j.1467-8667.2010.00661.x.
9. Dunn, J.C. (1973), A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well- Separated Clusters, J. Cybernetics, vol. 3, pp. 32-57.
10. Filippone M., Camastra F., Masulli F. & Rovetta S. (2008), A survey of kernel and spectral methods for clustering. Pattern recognition, 41.
11. Gadani, J.P. (1981), System Effectiveness Evaluation using Star and Delta Transformations. IEEE Trans. Rel., 30(1), 43-47.
12. Graepel T. & Obermayer K. (1998), Fuzzy topographic kernel clustering, in: W. Brauer (Ed.), Proceedings of the Fifth GI Workshop Fuzzy Neuro-Systems, 98, pp. 90-97.
13. Gómez C., Sánchez-Silva M., Dueñas-Osorio & Rosowsky D. (2010), Hierarchical infrastructure network representation methods for risk based decision making. [forthcoming].
14. Haldaki, M., Batistakis, Y. & Vazirgiannis, M. (2002), Cluster Validity Methods: Part I. SIGMOD Record. Vol.31 No. 2, June 2002.
15. Inokuchi R. & Miyamoto S. (2004), LVQ clustering and SOM using a kernel function, in: Proceedings of IEEE International Conference on Fuzzy Systems, vol. 3, pp. 1497-1500.
16. Javanbarg, M.B., Scawthorn, C., Kiyono, J. & Ono, Y. (2009), Reliability Analysis of Infrastructure Networks Using OBDD. Proc ICOSSAR 2009, Osaka, Japan.
17. Kearns, M.J. & Vazirani, U.V. (1994), An introduction to computational learning theory. Cambridge, Mass: MIT Press.
18. Maturana H.R. & Varela F.J. (1980), Autopoiesis and cognition: The realization of the living. D. Reidel, Dordrecht, The Netherlands.
19. McQueen J. (1967), Some methods for classification and analysis of multivariate observations. Computer and Chemistry, 4, 257-272.
20. Meila M. & Shi J. (2000), Learning segmentation by random walks, in: NIPS, pp. 873-879.
21. Newman M.E.J. (2004), Detecting community structure in networks. Eur. Phys. J. B 38, 321-330.
22. Newman M.E.J. & Leicht E.A. (2007), Mixture models and exploratory analysis in networks. PNAS, Vol.104 (23), 9564-9569.
23. Ng A., Jordan M. & Weiss Y. (2001), On spectral clustering, analysis and an algorithm. In Advances in Neural Information processing systems, 14.
24. Peng J. & Xia Y. (2005), A New Theoretical Framework for K-Means-Type Clustering, StudFuzz 180, 79-96. Springer-Verlag Berlin Heidelberg.
25. Satyanarayana A. & Chang M., (1983), Network reliability and the factoring theorem, Networks 13, 107-120.
26. Scholkopf B., Smola A.J. & Muller K.R. (1998), Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput., 10 (5) 1299-1319.
27. Scholkopf B. & Smola (2000), Learning with kernels. The MIT Press: Cambridge, Massachusetts.
28. Serrano, A.J., Soria, E., Martin, J.D., Magdalena, R. & Gomez, J. (2010), Feature selection using ROC curves on classification problems. 2010 IEEE World Congress on Computational Intelligence.
29. Shi, J. & Malik, J. (2000), Normalized cuts and image segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905.
30. van Dongen S. (2000), Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht, May.
31. Verma D. & Meila M. (2005), A comparison of spectral clustering algorithms, Technical Report, Department of CSE University of Washington Seattle, WA, 98195-2350.
32. Wasserman S. & Faust K. (1994), Social Networks analysis: methods and applications. Cambridge University Press.
33. Yu S.X. & Shi J. (2003), Multiclass spectral clustering, in International Conference on Computer Vision.