Hierarchical model-based clustering of large datasets through fractionation and refractionation

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2002, Pages 183-190

  Tantrum, Jeremy ^a   Murua, Alejandro ^b   Stuetzle, Werner ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Insightful Corporation   (United States)

Author keywords

Fractionation; Model based clustering; Refractionation

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA STRUCTURES; ESTIMATION; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILITY DENSITY FUNCTION; STATISTICAL METHODS;

MODEL BASED CLUSTERING METHOD; NONPARAMETRIC CLUSTERING METHODS;

PATTERN RECOGNITION;

EID: 0242540462     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/775047.775074     Document Type: Conference Paper

References (18)

1. J. Allan, J. Carbonell, G. Doddington, J. Yamron, and Y. Yang, Topic detection and tracking pilot study final report, 1998.
2. M. Ankerst, M. Breuning, H. Kriegel, and J. Sander. Optics: Ordering points to identify the clustering structure. In Proceedings, ACM SIGMOD International Conference on Management of Data (SIGMOD'99), pages 49-60, 1999.
3. J. D. Banfield and A. Raftery. Model-based Gaussian and non-Gaussian clustering. Biometrics, 49:803-821, 1993.
4. M. Berry, S. Dumais, and G. O'Brien. Using linear algebra for intelligent information retrieval. SIAM Review, 37(4):573-595, 1995.
5. P. Bradley, U. Fayyad, and C. Reina. Scaling clustering algorithms to large datasets. In Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining (KDD98), 1998.
6. P. Bradley, U. Fayyad, and C. Reina. Scaling EM (expectation-maximization) clustering to large databases. Technical Report MSR-TR-98-35, Microsoft Research, 1999.
7. D. R. Cutting, D. R. Karger, J. O. Pedersen, and J. W. Tukey. Scatter/gather: A cluster-based approach to browsing large document collections. In 15th Ann Int'l SIGR, pages 318-329, 1992.
8. P. Domingos and G. Hulten. Learning from infinite data in finite time. In Advances in Neural Information Processing Systems 14, 2002.
9. S. Dumais. Improving the retrieval of information from external sources. Behavior Research Methods, Instruments & Computers, 23(2):229-236, 1991.
10. M. Ester, H. Krieger, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 226-231, 1996.
11. E. B. Fowlkes and C. L. Mallows. A method for comparing two hierarchical clusterings. J. American Statistical Association, 78:553-569, 1983.
12. C. Fraley and A. Raftery. How many clusters? which clustering method? - answers via model-based cluster analysis. The Computer Journal, 41:578-588, 1998.
13. J. Hartigan. Statistical theory in clustering. Journal of Classification, 2:63-76, 1985.
14. G. E. Hinton, P. Dayan, and M. Revow. Modeling the manifolds of images of handwritten digits. IEEE Transactions on Neural Networks, 8(1):65-74, 1997.
15. G. McLachlan, and D. Peel. Finite Mixture Models. John Wiley & Sons, 2000.
16. G. Schwartz. Estimating the dimension of a model. Annals of Statistics, 6:497-511, 1978.
17. D. Scott. Multivariate Density Estimation. Wiley, 1992.
18. D. Wishart. Mode analysis: A generalization of nearest neighbor which reduces chaining effects. In A. Cole, editor, Numerical Taxonomy, pages 282-311. Academic Press, 1969.