Probabilistic consensus clustering using evidence accumulation

Machine Learning

Volumn 98, Issue 1-2, 2015, Pages 331-357

  Lourenço, André ^a,b   Rota Bulò, Samuel ^c   Rebagliati, Nicola ^d   Fred, Ana L N ^b,e   Figueiredo, Mário A T ^b,e   Pelillo, Marcello ^c  

^a INSTITUTO SUPERIOR DE ENGENHARIA DE LISBOA   (Portugal)

^b INSTITUTO DE TELECOMUNICAÇÕES   (Portugal)

^c DAIS   (Italy)

^d VTT TECHNICAL RESEARCH CENTRE OF FINLAND   (Finland)

^e INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

Bregman divergence; Consensus clustering; Ensemble clustering; Evidence Accumulation

Indexed keywords

CLUSTER ANALYSIS; MATRIX ALGEBRA;

BREGMAN DIVERGENCES; CO-OCCURRENCE PROBABILITY; CONSENSUS CLUSTERING; CORRESPONDENCE PROBLEMS; ENSEMBLE CLUSTERING; EVIDENCE ACCUMULATION; OPTIMIZATION ALGORITHMS; PROBABILISTIC ASSIGNMENTS;

CLUSTERING ALGORITHMS;

EID: 84920708410     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-013-5339-6     Document Type: Article

References (45)

1. Arora, R., Gupta, M., Kapila, A., & Fazel, M. (2011). Clustering by left-stochastic matrix factorization. In L. Getoor & T. Scheffer (Eds.), ICML (pp. 761–768). Omnipress.
2. Ayad, H., & Kamel, M. S. (2008). Cumulative voting consensus method for partitions with variable number of clusters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(1), 160–173.
3. Banerjee, A., Krumpelman, C., Basu, S., Mooney, R. J., & Ghosh, J. (2005a). Model-based overlapping clustering. In Int. conf. on knowledge discovery and data mining (pp. 532–537).
4. Banerjee, A., Merugu, S., Dhillon, I., & Ghosh, J. (2005b). Clustering with Bregman divergences. Journal of Machine Learning Research, 6, 1705–1749.
5. Bezdek, J. (1981). Pattern recognition with fuzzy objective function algorithms. Norwell: Kluwer Academic.
6. Bezdek, J., & Hathaway, R. (2002). VAT: a tool for visual assessment of (cluster) tendency. In Proceedings of the 2002 international joint conference on neural networks 2002, IJCNN’02 (Vol. 3, pp. 2225–2230).
7. Boyd, S., & Vandenberghe, L. (2004). Convex optimization (1st ed.). Cambridge: Cambridge University Press.
8. Cui, Y., Fern, X. Z., & Dy, J. G. (2010). Learning multiple nonredundant clusterings. In Transactions on Knowledge Discovery from Data (TKDD) (Vol. 4, pp. 1–32).
9. Dhillon, I. S., Mallela, S., & Kumar, R. (2003). A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research, 3, 1265–1287.
10. Dimitriadou, E., Weingessel, A., & Hornik, K. (2002). A combination scheme for fuzzy clustering. In AFSS’02 (pp. 332–338).
11. Färber, I., Günnemann, S., Kriegel, H., Kröger, P., Müller, E., Schubert, E., Seidl, T., & Zimek, A. (2010). On using class-labels in evaluation of clusterings. In MultiClust: 1st international workshop on discovering, summarizing and using multiple clusterings.
12. Fern, X. Z., & Brodley, C. E. (2004). Solving cluster ensemble problems by bipartite graph partitioning. In Proc. ICML ’04.
13. Frank, A., & Asuncion, A. (2012). In UCI machine learning repository.
14. Fred, A. (2001). Finding consistent clusters in data partitions. In J. Kittler & F. Roli (Eds.), Multiple classifier systems (Vol. 2096, pp. 309–318). Berlin: Springer.
15. Fred, A., & Jain, A. (2002). Data clustering using evidence accumulation. In Proc. of the 16th int’l conference on pattern recognition (pp. 276–280).
16. Fred, A., & Jain, A. (2005). Combining multiple clustering using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 835–850.
17. Fred, A., & Jain, A. (2006). Learning pairwise similarity for data clustering. In Proc. of the 18th int’l conference on pattern recognition (ICPR), Hong Kong (Vol. 1, pp. 925–928).
18. Ghosh, J., & Acharya, A. (2011). Cluster ensembles Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(4), 305–315.
19. Karypis, G., Aggarwal, R., Kumar, V., & Shekhar, S. (1997). Multilevel hypergraph partitioning: applications in VLSI domain. In Proc. design automation conf.
20. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5228–5235.
21. Heller, K., & Ghahramani, Z. (2007). A nonparametric Bayesian approach to modeling overlapping clusters. In Int. conf. AI and statistics.
22. Jain, A. K., & Dubes, R. (1988). Algorithms for clustering data. New York: Prentice Hall.
23. Jardine, N., & Sibson, R. (1968). The construction of hierarchic and non-hierarchic classifications. Computer Journal, 11, 177–184.
24. Kachurovskii, I. R. (1960). On monotone operators and convex functionals. Uspehi Matematičeskih Nauk, 15(4), 213–215.
25. Karypis, G., & Kumar, V. (1998). Multilevel algorithms for multi-constraint graph partitioning. In Proceedings of the 10th supercomputing conference.
26. Lee, D. D., & Seung, H. S. (2000). Algorithms for non-negative matrix factorization. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), NIPS (pp. 556–562). Cambridge: MIT Press.
27. Lourenço, A., Fred, A., & Figueiredo, M. (2011). A generative dyadic aspect model for Evidence Accumulation Clustering. In Proc. 1st int. conf. similarity-based pattern recognition, SIMBAD’11 (pp. 104–116). Berlin/Heidelberg: Springer.
28. Lourenço, A., Fred, A., & Jain, A. K. (2010). On the scalability of evidence accumulation clustering. In Proc. 20th international conference on pattern recognition (ICPR), Istanbul, Turkey.
29. Mei, J. P., & Chen, L. (2010). Fuzzy clustering with weighted medoids for relational data. Pattern Recognition, 43(5), 1964–1974.
30. Meila, M. (2003). Comparing clusterings by the variation of information. In Springer (Ed.), Proc. of the sixteenth annual conf. of computational learning theory, COLT.
31. Nepusz, T., Petróczi, A., Négyessy, L., & Bazsó, F. (2008). Fuzzy communities and the concept of bridgeness in complex networks. Physical Review A, 77, 016107.
32. Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001). On spectral clustering: analysis and an algorithm. In NIPS (pp. 849–856). Cambridge: MIT Press.
33. Paatero, P., & Tapper, U. (1994). Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5(2), 111–126.
34. Punera, K., & Ghosh, J. (2007). Soft consensus clustering. In Advances in fuzzy clustering and its applications. New York: Wiley.
35. Punera, K., & Ghosh, J. (2008). Consensus-based ensembles of soft clusterings. Applied Artificial Intelligence, 22(7&8), 780–810.
36. Rota Bulò, S., Lourenço, A., Fred, A., & Pelillo, M. (2010). Pairwise probabilistic clustering using evidence accumulation. In Proc. 2010 int. conf. on structural, syntactic, and statistical pattern recognition, SSPR&SPR’10 (pp. 395–404).
37. Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 22(8), 888–905.
38. Steyvers, M., & Griffiths, T. (2007). Latent semantic analysis: a road to meaning. In Probabilistic topic models. Laurence Erlbaum.
39. Strehl, A., & Ghosh, J. (2003). Cluster ensembles—a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.
40. Topchy, A., Jain, A., & Punch, W. (2003). Combining multiple weak clusterings. In IEEE intl. conf on data mining, Melbourne (pp. 331–338).
41. Topchy, A., Jain, A., & Punch, W. (2004). A mixture model of clustering ensembles. In Proc. of the SIAM conf. on data mining.
42. Topchy, A., Jain, A. K., & Punch, W. (2005). Clustering ensembles: models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12), 1866–1881.
43. Wang, H., Shan, H., & Banerjee, A. (2009). Bayesian cluster ensembles. In 9th SIAM int. conf. on data mining.
44. Wang, H., Shan, H., & Banerjee, A. (2011). Bayesian cluster ensembles. Statistical Analysis and Data Mining, 4(1), 54–70.
45. Wang, P., Domeniconi, C., & Laskey, K. B. (2010). Nonparametric Bayesian clustering ensembles. In ECML PKDD’10 (pp. 435–450).