Methods for merging Gaussian mixture components

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Baudry JP, Raftery AE, Celeux G, Lo K, Gottardo R (2008) Combining mixture components for clustering. Technical report 540, University of Washington, Seattle

Biernacki C, Celeux G, Govaert G (2000) Assessing a mixture model for clustering with the integrated completed likelihood. IEEE T Pattern Anal 22: 719–725

Campbell NA, Mahon RJ (1974) A multivariate study of variation in two species of rock crab of genus Leptograpsus. Aust J Zool 22: 417–425

Davies PL (1995) Data features. Stat Neerl 49: 185–245

Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97: 611–631

Fraley C, Raftery AE (2003) Enhanced software for model-based clustering, density estimation and discriminant analysis. J Classif 20: 263–286

Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, New York

Hartigan JA, Hartigan PM (1985) The dip test of unimodality. Ann Stat 13: 70–84

Hastie T, Tibshirani R (1996) Discriminant analysis by Gaussian mixtures. J Roy Stat Soc B Met 58: 155–176

Hennig C (2005) Asymmetric linear dimension reduction for classification. J Comput Graph Stat 13: 930–945

Hennig C (2010) Ridgeline plot and clusterwise stability as tools for merging Gaussian mixture components. In: Locarek-Junge H, Weihs C (eds) Classification as a tool for research. Springer, Berlin, accepted for publication

Hennig C, Coretto P (2008) The noise component in model-based cluster analysis. In: Preisach C, Burkhard H, Schmidt-Thieme L, Decker R (eds) Data analysis, machine learning and applications. Springer, Berlin, pp 127–138

Keribin C (2000) Consistent estimation of the order of a mixture model. Sankhya Ser A 62: 49–66

Li J (2004) Clustering based on a multilayer mixture model. J Comput Graph Stat 14: 547–568

Matusita K (1971) Some properties of affinity and applications. Ann I Stat Math 23: 137–155

McLachlan GJ, Peel D (2000) Finite mixture models. Wiley, New York

Qiu W, Joe H (2006) Generation of random clusters with specified degree of separation. J Classif 23: 315–334

Ray S, Lindsay BG (2005) The topography of multivariate normal mixtures. Ann Stat 33: 2042–2065

Redner RA, Walker HF (1984) Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev 26: 195–239

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Street WN, Wolberg WH, Mangasarian OL (1993) Nuclear feature extraction for breast tumor diagnosis. IS & T/SPIE 1993 international symposium on electronic imaging: science and technology, vol 1905, San Jose, CA, pp 861–870

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Tibshirani R, Walther G (2005) Cluster validation by prediction strength. J Comput Graph Stat 14: 511–528

Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a dataset via the gap statistic. J Roy Stat Soc B Met 63: 411–423

Ueda N, Nakano R, Ghahramani Z, Hinton GE (2000) SMEM algorithm for mixture models. Neural Comput 12: 2109–2128