Is VAT really single linkage in disguise?

Annals of Mathematics and Artificial Intelligence

Volumn 55, Issue 3-4, 2009, Pages 237-251

  Havens, Timothy C ^a   Bezdek, James C ^a   Keller, James M ^a   Popescu, Mihail ^a   Huband, Jacalyn M ^b  

^a UNIVERSITY OF MISSOURI   (United States)

^b UNIVERSITY OF WEST FLORIDA   (United States)

Author keywords

Clustering; Graph theory; Minimal spanning tree; Relational data; Single linkage; Visual assessment of cluster tendency

Indexed keywords

EID: 72149092690     PISSN: 10122443     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10472-009-9157-2     Document Type: Article

References (29)

1. Bezdek, J., Hathaway, R.: VAT: a tool for visual assessment of (cluster) tendency. In: Proc. IJCNN 2002, pp. 2225-30. Piscataway (2002).
2. Bezdek, J., Hathaway, R., Huband, J.: Visual assessment of clustering tendency for rectangular dissimilarity matrices. IEEE Trans. Fuzzy Syst. 15(5), 890-903 (2007).
3. Bezdek, J., Keller, J., Krishnapuram, R., Pal, N.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer, Norwell (1999).
4. Ding, Y., Harrison, R.: Relational visual cluster validity (RVCV). Pattern Recogn. Lett. 28, 2071-2079 (2007).
5. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2000).
6. Dunn, J.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32-57 (1974).
7. Gower, J., Ross, G.: Minimum spanning trees and single linkage cluster analysis. Appl. Stat. 18, 54-64 (1969).
8. Harary, F.: Graph Theory. Addison-Wesley, Reading (2004).
9. Hartigan, J.: Clustering Algorithms. Wiley, New York (1975).
10. Hathaway, R., Bezdek, J.: Visual cluster validity for prototype generator clustering models. Pattern Recogn. Lett. 24, 1563-1569 (2003).
11. Hathaway, R., Bezdek, J., Huband, J.: Scalable visual asseessment of cluster tendency for large data sets. Pattern Recogn. 39(7), 1315-1324 (2006).
12. Havens, T., Bezdek, J., Keller, J., Popescu, M.: Clustering in ordered dissimilarity data. Int. J. Intell. Syst. 24(5), 504-528 (2009).
13. Huband, J., Bezdek, J.: Computational Intelligence: Research Frontiers, chap. VCV2-Visual Cluster Validity, pp. 293-308. Springer, Berlin (2008).
14. Huband, J., Bezdek, J., Hathaway, R.: Revised visual assessment of (cluster) tendency (reVAT). In: Proc. NAFIPS, pp. 101-104. IEEE Press, Banff (2004).
15. Huband, J., Bezdek, J., Hathaway, R.: bigVAT: visual assessment of cluster tendency for large data sets. Pattern Recogn. 38(11), 1875-1886 (2005).
16. Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988).
17. Kruskal, J.: On the shortest spanning subtree of a graph and the traveling salesman problem. In: Proc. of the Am. Math. Soc., vol. 7, pp. 48-50 (1956).
18. Lynch, N.: Distributed Algorithms, 4th edn. Morgan Kaufmann, San Fransisco (1996).
19. Myllyharju, J., Kivirikko, K.: Collagens, modifying enzymes, and their mutation in humans, flies, and worms. Trends Genet. 20(1), 33-43 (2004).
20. Notsu, A., Ichihashi, H., Honda, K., Katai, O.: Visualization of balancing systems based on naive psychological approaches. AI Soc. 23(2), 281-296 (2007).
21. Popescu, M., Bezdek, J., Keller, J., Havens, T., Huband, J.: A new cluster validity measure for bioinformatics relational datasets. In: Proc. FUZZ-IEEE, pp. 726-731. Hong Kong, China (2008).
22. Popescu, M., Keller, J., Mitchell, J., Bezdek, J.: Functional summarization of gene product clusters using Gene Ontology similarity measures. In: Proc. 2004 ISSNIP, pp. 553-559. IEEE, Piscataway (2004).
23. Prim, R.: Shortest connection networks and some generalisations. Bell Syst. Tech. J. 36, 1389-1401 (1957).
24. Sledge, I., Havens, T., Huband, J., Bezdek, J., Keller, J.: Finding the number of clusters in ordered dissimilarities. Soft Comput. 13, 1125-1142 (2009).
25. The Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32, D258-D261 (2004).
26. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic, San Diego (2006).
27. Hubbard, T. J. P., et al.: Ensembl 2009. Nucleic Acids Res. 37, D690-D697 (2009).
28. Wang, L., Leckie, C., Rao, K., Bezdek, J.: Automatically determining the number of clusters from unlabeled data sets. IEEE Trans. Knowl. Data Eng. 21(3), 335-350 (2009).
29. Yang, S., Luo, S., Li, J.: Advanced data mining and applications. In: Lecture Notes in Computer Science, vol. 4093, chap. A novel visual clustering algorithm for finding community in complex systems, pp. 396-403. Springer, Berlin (2006).