Bayesian cluster ensembles

Statistical Analysis and Data Mining

Volumn 4, Issue 1, 2011, Pages 54-70

  Wang, Hongjun ^a   Shan, Hanhuai ^b   Banerjee, Arindam ^b  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

^b University of Minnesota   (United States)

Author keywords

Bayesian models; Cluster ensembles

Indexed keywords

APPROXIMATION ALGORITHMS; BAYESIAN NETWORKS; CLUSTERING ALGORITHMS; LEARNING ALGORITHMS;

A-STABLE; BAYESIAN; BAYESIAN MODELLING; CLUSTER ENSEMBLES; CLUSTERINGS; CONSENSUS CLUSTERING; ENSEMBLE ALGORITHMS; MISSING VALUES; MULTIPLE BASIS; ROBUST CONSENSUS;

CLUSTER ANALYSIS;

EID: 79551693227     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.10098     Document Type: Article

References (20)

1. A. Topchy, A. Jain, and W. Punch, A mixture model for clustering ensembles, In SDM, 2004, 379-390.
2. A. Strehl and J. Ghosh, Cluster ensembles-a knowledge reuse framework for combining multiple partitions, JMLR 3 (2002), 583-617.
3. L. Kuncheva and D. Vetrov, Evaluation of stability of k-means cluster ensembles with respect to random initialization, PAMI 28 (2006), 1798-1808.
4. G. Jagannathan and R. Wright, Privacy-preserving distributed k-means clustering over arbitrarily partitioned data, In KDD, 2005, 593-599.
5. X. Fern and C. Brodley, Solving cluster ensemble problems by bipartite graph partitioning, In ICML, 2004, 281-288.
6. M. Al-Razgan and C. Domeniconi, Weighted cluster ensemble, In SDM, 2006, 258-269.
7. X. Fern and C. Brodley, Random projection for high dimensional data clustering: a cluster ensemble approach, In ICML, 2003, 186-193.
8. G. Karypis and V. Kumar, A fast and high quality multilevel scheme for partitioning irregular graphs, SIAM J Sci Comput 20 (1999), 359-392.
9. G. Karypis, R. Aggarwal, V. Kumar, and S. Shekhar, Multilevel hypergraph partitioning: applications in VLSI design, In ACM/IEEE Design Automation Conference, 1997, 526-529.
10. A. Fred and A. Jain, Data clustering using evidence accumulation, In ICPR, 2002, 276-280.
11. T. Li, C. Ding, and M. Jordan, Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization, In ICDM, 2007.
12. P. Kellam, X. Liu, N. Martin, C. Orengo, S. Swift, and A. Tucker, Comparing, contrasting and combining clusters in viral gene expression data, In Workshop on Intelligent Data Analysis in Medicine and Pharmocology, 2001, 56-62.
13. S. Monti, P. Tamayo, J. Mesirov, and T. Golub, Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data, Mach Learn J 52 (2003), 91-118.
14. A. Banerjee and H. Shan, Latent Dirichlet conditional naive-Bayes models, In ICDM, 2007, 421-426.
15. H. Shan and A. Banerjee, Mixed-membership naive Bayes models. Data Mining and Knowledge Discovery, 2010.
16. D. Blei, A. Ng, and M. Jordan, Latent Dirichlet allocation, JMLR 3 (2003), 993-1022.
17. R. Neal and G. Hinton, A view of the EM algorithm that justifies incremental, sparse, and other variants, In Learning in Graphical Models, 1998, 355-368.
18. Z. Zhou and W. Tang, Clusterer ensemble, Knowl Based Syst 1 (2006), 77-83.
19. J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
20. B. Frey and D. Dueck, Clustering by passing messages between data points, Science 315 (2007), 972-976.