A simulation study to compare robust clustering methods based on mixtures

Advances in Data Analysis and Classification

Volumn 4, Issue 2, 2010, Pages 111-135

  Coretto, Pietro ^a   Hennig, Christian ^b  

^a UNIVERSITY OF SALERNO   (Italy)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Gaussian mixture; Mixture of t distributions; Model based clustering; Noise component

Indexed keywords

CLUSTERING ALGORITHMS; COMPUTER SIMULATION; GAUSSIAN DISTRIBUTION; IMAGE SEGMENTATION;

DEGREES OF FREEDOM; FIXED NUMBERS; GAUSSIAN MIXTURES; IN STATS; MIXTURE MODEL; MIXTURE OF GAUSSIANS; MODEL-BASED CLUSTERING; NOISE COMPONENTS; REAL LINE; ROBUST CLUSTERING; SIMULATION STUDIES; T DISTRIBUTION; UNIFORM DISTRIBUTION; UNIVERSITY COLLEGE LONDON;

MIXTURES;

EID: 77955847680     PISSN: 18625347     EISSN: 18625355     Source Type: Journal    
DOI: 10.1007/s11634-010-0065-4     Document Type: Article

References (18)

1. Banfield J, Raftery AE (1993) Model-based gaussian and non-gaussian clustering. Biometrics 49: 803-821.
2. Coretto P (2008) The noise component in model-based clustering. PhD thesis, Department of Statistical Science, University College London. http://www. ontherubicon. com/pietro/docs/phdthesis. pdf.
3. Cuesta-Albertos JA, Gordaliza A, Matrán C (1997) Trimmed k-means: an attempt to robustify quantizers. Ann Stat 25: 553-576.
4. Fraley C, Raftery AE (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J 41: 578-588.
5. Fraley C, Raftery AE (2006) Mclust version 3 for r: normal mixture modeling and model-based clustering. Technical report 504, Department of Statistics, University of Washington.
6. Gallegos MT, Ritter G (2005) A robust method for cluster analysis. Ann Stat 33(5): 347-380.
7. García-Escudero LA, Gordaliza A, Matrán C, Mayo-Iscar A (2008) A general trimming approach to robust cluster analysis. Ann Stat 38(3): 1324-1345.
8. Hathaway RJ (1985) A constrained formulation of maximum-likelihood estimation for normal mixture distributions. Ann Stat 13: 795-800.
9. Hennig C (2004) Breakdown points for maximum likelihood estimators of location-scale mixtures. Ann Stat 32(4): 1313-1340.
10. Hennig C (2005) Robustness of ML estimators of location-scale mixtures. In: Baier D, Wernecke KD (eds) Innovations in classification. Data science, and information systems. Springer, Heidelberg, pp 128-137.
11. Hennig C, Coretto P (2008) The noise component in model-based cluster analysis. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R (eds) Data analysis, machine learning and applications. Springer, Berlin, pp 127-138.
12. Hosmer DW (1978) Comment on "Estimating mixtures of normal distributions and switching regressions" by R. Quandt and J. B. Ramsey. J Am Stat Assoc 73(364): 730-752.
13. Karlis D, Xekalaki E (2003) Choosing initial values for the EM algorithm for finite mixtures. Comput Stat Data Anal 41(3-4): 577-590.
14. Liu C (1997) ML estimation of the multivariate t distribution and the EM algorithms. J Multivar Anal 63: 296-312.
15. McLachlan G, Krishnan T (1997) The EM algorithm and extensions. Wiley, New York.
16. McLachlan G, Peel D (2000) Robust mixture modelling using the t-distribution. Stat Comput 10(4): 339-348.
17. Neykov N, Filzmoser P, Dimova R, Neytchev P (2007) Robust fitting of mixtures using the trimmed likelihood estimator. Comput Stat Data Anal 17(3): 299-308.
18. Redner R, Walker HF (1984) Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev 26: 195-239.