Scalable model-based cluster analysis using clustering features

Pattern Recognition

Volumn 38, Issue 5, 2005, Pages 637-649

  Jin, Huidong ^a   Leung, Kwong Sak ^b   Wong, Man Leung ^c   Xu, Zong Ben ^d  

^a CSIRO   (Australia)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^c LINGNAN UNIVERSITY   (Hong Kong)

^d XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Cluster analysis; Clustering feature; Convergence; Data mining; Expectation maximization; Gaussian mixture model; Scalable

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; DATA MINING; ITERATIVE METHODS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; MIXTURES; PARAMETER ESTIMATION; PROBABILITY; SET THEORY;

CLUSTER ANALYSIS; CLUSTERING FEATURE; CONVERGENCE; EXPECTATION MAXIMIZATION; GAUSSIAN MIXTURE MODEL; SCALABLE;

FEATURE EXTRACTION;

EID: 13644262819     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2004.07.012     Document Type: Article

References (23)

1. J. Han, and M. Kamber Data Mining Concepts and Techniques 2001 Morgan Kaufmann Publishers San Francisco, CA, USA
2. H.-D. Jin, K.-S. Leung, and M.-L. Wong Scaling-up model-based clustering algorithm by working on clustering features Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning 2002 569 575
3. P. Bradley, U. Fayyad, C. Reina, Clustering very large databases using EM mixture models, in: Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, 2000, pp. 76-80.
4. T. Zhang, R. Ramakrishnan, and M. Livny BIRCH a new data clustering algorithm and its applications Data Min. Knowl. Disc. 1 2 1997 141 182
5. H.-D. Jin, W. Shum, K.-S. Leung, and M.-L. Wong Expanding self-organizing map for data visualization and cluster analysis Inform. Sci. 163 1-3 2004 157 173
6. C. Palmer, and C. Faloutsos Density biased sampling an improved method for data mining and clustering Proceedings of the 2000 ACM International Conference on Management of Data 2000 82 92
7. S. McKenna, S. Gong, and Y. Raja Modelling facial colour and identity with Gaussian mixtures Pattern Recognition 31 1998 1883 1892
8. C. Fraley Algorithms for model-based Gaussian hierarchical clustering SIAM J. Sci. Comput. 20 1 1999 270 281
9. M. Figueiredo, and A.K. Jain Unsupervised learning of finite mixture models IEEE Trans. Pattern Anal. Mach. Intell. 24 3 2002 381 396
10. G. McLachlan, R. Bean, and D. Peel A mixture model-based approach to the clustering of microarray expression data Bioinformatics 18 2002 413 422
11. I. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White Model-based clustering and visualization of navigation patterns on a web site Data Min. Knowl. Disc. 7 4 2003 399 424
12. G. Celeux, and G. Govaert Gaussian parsimonious clustering models Pattern Recognition 28 5 1995 781 793
13. P. Cheeseman, and J. Stutz Bayesian classification (AutoClass) theory and results U. Fayyad Advances in Knowledge Discovery and Data Mining 1996 Menlo Park, CA USA 153 180
14. A. Dempster, N. Laird, and D. Rubin Maximum-likelihood from incomplete data via the EM algorithm J. Roy. Statist. Soc. Ser. B 39 1977 1 38
15. J. Ma, L. Xu, and M. Jordan Asymptotic convergence rate of the EM algorithm for Gaussian mixtures Neural Comput. 12 12 2000 2881 2907
16. G. McLachlan, and T. Krishnan The EM Algorithm and Extensions 1997 Wiley New York
17. R. Neal, and G. Hinton A view of the EM algorithm that justifies incremental, sparse, and other variants M. Jordan Learning in Graphical Models 1999 MIT Press Cambridge, MA 355 368
18. J. Shanmugasundaram, U. Fayyad, and P. Bradley Compressed data cubes for OLAP aggregate query approximation on continuous dimensions Proceedings of the Fifth ACM SIGKDD San Diego, USA 1999 223 232
19. M. Meilǎ, and D. Heckerman An experimental comparison of model-based clustering methods Mach. Learn. 42 1/2 2001 9 29
20. B. Thiesson, C. Meek, and D. Heckerman Accelerating EM for large databases Mach. Learn. 45 2001 279 299
21. A. Moore, Very fast EM-based mixture model clustering using multiresolution KD-trees, in: M. Kearns, D. Cohn (Eds.), Advances in Neural Information Processing Systems, vol. 11, 1999, pp. 543-549.
22. H.-D. Jin, Scalable model-based clustering algorithms for large databases and their applications, Ph.D. Thesis, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, August 2002.
23. W. DuMouchel, C. Volinsky, T. Johnson, C. Cortes, and D. Pregibon Squashing flat files flatter Proceedings of the Fifth ACM SIGKDD San Diego, CA, USA 1999 6 15