Classification of large data sets with mixture models via sufficient EM

Computational Statistics and Data Analysis

Volumn 51, Issue 11, 2007, Pages 5416-5428

  Steiner, P M ^a   Hudec, M ^b  

^a INSTITUTE FOR ADVANCED STUDIES   (Austria)

^b UNIVERSITY OF VIENNA   (Austria)

Author keywords

Data compression; EM algorithm; Mixture models; Unsupervised classification; Web usage mining

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA COMPRESSION; DATA MINING; ESTIMATION; MATHEMATICAL MODELS; STATISTICAL METHODS;

EM ALGORITHM; MIXTURE MODELS; UNSUPERVISED CLASSIFICATION; WEB-USAGE MINING;

DATA STRUCTURES;

EID: 34247860759     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2006.09.014     Document Type: Article

References (18)

1. Anderson T. An Introduction to Multivariate Statistical Analysis. second ed (1984), Wiley, New York
2. Bradley, P., Fayyad, U., Reina, C., 1999. Scaling EM (expectation-maximization) clustering to large databases. Technical Report MSR-TR-98-35, Microsoft Research.
3. Dempster A., Laird N., and Rubin D. Maximum likelihood from incomplete data via EM algorithm. J. Roy. Statist. Soc. B 39 (1977) 1-38
4. DuMouchel W. Data squashing: constructing summary data sets. In: Abello J. (Ed). Handbook of Massive Data Sets (2001), Kluwer Academic Publishers, Dordrecht, MA
5. Fukunaga K. Introduction to Statistical Pattern Recognition. second ed (1990), Academic Press, Boston
6. Guha, S., Rastogi, R., Shim, K., 1998. CURE: an efficient clustering algorithm for large databases. In: International Conference on Management of Data, ACM SIGMOD.
7. Hinneburg, A., Keim, D., 1999. Optimal grid-clustering: towards breaking the curse of dimensionality in high-dimensional clustering. In: Proceedings of the 25th VLDB Conference, Edinburgh.
8. Hudec M., Grossmann W., and Kurzawa R. Web-usage mining in e-commerce. Internat. J. Electron. Business 5 (2004) 480-492
9. McLachlan G., and Peel D. Finite Mixture Models (2000), Wiley, New York
10. Pötzelberger, K., Strasser, H., 1997. Data compression by unsupervised classification. Technical Report, Department of Statistics, Vienna University of Economics and Business Administration.
11. Pötzelberger K., and Strasser H. Clustering and quantization by MSP-partitions. Statistics and Decisions 19 (2001) 331-371
12. Rocke D. Constructive statistics: estimators, algorithms, and asymptotics. Comput. Sci. Statist. 30 (1998) 3-14
13. Rocke D., and Dai J. Sampling and subsampling for cluster analysis in data mining. Data Mining and Knowledge Discovery 7 (2003) 215-232
14. SPSS Inc., 2003. Clementine 9.0 Algorithms Guide.
15. Steiner, P., 2005. Identification of mixture models with compressed data: sufficient EM strategies for prototypes representing large data sets. Ph.D. Thesis, University of Vienna. URL 〈http://www.equi.at/stp.htm〉.
16. Strasser, H., 2000. Reduction of complexity. Technical Report, Department of Statistics, Vienna University of Economics and Business Administration.
17. Wang W., Yang J., and Muntz R. STING: a statistical information grid approach to spatial data mining. In: Jarke M., Carey M., Dittrich K., Lochovsky F., Loucopoulos P., and Jeusfeld M. (Eds). Twenty-Third International Conference on Very Large Data Bases (1997), Morgan Kaufmann, Athens, Greece
18. Zhang, T., Ramakrishnan, R., Livny, M., 1996. BIRCH: an efficient data clustering method for very large databases. In: ACM SIGMOD International Conference on Management of Data, Montreal, Canada.