Abstract
A mixture model is a probability model for representing subpopulations within a data set. The mixture model is built up from a weighted combination of component probability distributions. Mixture models can be estimated by attribution partial membership to the component distributions to individual observations in the data set.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Autoclass (2010) http://ti.arc.nasa.gov/project/autoclass/. Last Accessed 22 Mar 2010
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
Bradley PS, Reina CA, Fayyad UM (2000) Clustering very large databases using EM mixture models. In: 15th international conference on pattern recognition, vol 2. Barcelona, pp 2076
Chaudri K (2010) Learning mixture models. http://themachinelearningforum.org/index.php/overviews/34-colt-overviews/53-learning-mixture-models.html. June 2009, Last Accessed 21 Mar 2010
Dasgupta A, Hopcroft J, Kleinberg J, Sandler M (2005) On learning mixtures of heavy-tailed distributions. In: Proceedings of foundations of computer science, Pittsburg
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley-Interscience, New York
Figueiredo MAT, Jain AT (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24:381–396
Lindsey BG (1996) Mixture models: theory, geometry and applications. IMS Publishers, Hayward
McLachlan GJ, Peel D (2000) Finite mixture models. Wiley, New York
Mclust (2010) http://www.stat.washington.edu/mclust/. Last Accessed 22 Mar 2010
Rasmussen CE (2000) The infinite Gaussian mixture model. In: NIPS 12. MIT Press, Cambridge, pp 554–560
Redner RA, Walker HF (2004) Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev 26:195–239
Snob (2010) http://www.datamining.monash.edu.au/software/snob/. Last Accessed 22 Mar 2010
Srebo N, Shakhnarovich G, Roweis S (2006) An investigation of computational and informational limits in Gaussian mixture modeling. In: Proceedings of ICML, Pittsburgh
Xu L, Jordan MI (1996) On convergence properties of the EM algorithm for Gaussian mixtures. Neural Comput 8:129–151
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
Baxter, R.A. (2017). Mixture Model. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_552
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_552
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering