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Flexible mixture models for automatic clustering

version 0.40 (61.2 KB) by Statovic
This is a Matlab implementation of clustering (i.e., finite mixture models, unsupervised classification).

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Updated 01 Mar 2021

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SNOB is a Matlab implementation of finite mixture models. Snob uses the minimum message length criterion to estimate the structure of the mixture model (i.e., the number of sub-populations; which sample belongs to which sub-population) and estimate all mixture model parameters. SNOB allows the user to specify the desired number of sub-populations, however if this is not specified, SNOB will automatically try to discover this information. Currently, SNOB supports mixtures of the following distributions:

-Beta distribution
-Exponential distribution
-Univariate gamma distribution
-Geometric distribution
-Inverse Gaussian distribution
-Univariate Laplace distribution
-Gaussian linear regression
-Logistic regression
-Multinomial distribution
-Multivariate Gaussian distribution
-Negative binomial distribution
-Univariate normal distribution
-Poisson distribution
-Multivariate normal distribution (single factor analysis)
-von Mises-Fisher distribution
-Weibull distribution

The program is easy to use and allows missing data - all missing data should be coded as NaN. Examples of how to use the program are provided; see data/mm_example?.m.

UPDATE VERSION 0.4.0 (01/03/2021):
Latest updates:
-added two new distributions (von Mises Fisher and beta distribution; improved numerical accuracy for high-dimensional VMF mixtures coming up in a later update)
-added two examples of using snob
-improved numerical accuracy overall
-improved documenation

Cite As

Wallace, C. S. & Dowe, D. L. MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Statistics and Computing, 2000 , 10, pp. 73-83

Wallace, C. S. Intrinsic Classification of Spatially Correlated Data. The Computer Journal, 1998, 41, pp. 602-611

Wallace, C. S. Statistical and Inductive Inference by Minimum Message Length. Springer, 2005

Schmidt, D. F. & Makalic, E. Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions. AI 2012: Advances in Artificial Intelligence, Springer Berlin Heidelberg, 2012, 7691, pp. 672-682

Edwards, R. T. & Dowe, D. L. Single factor analysis in MML mixture modelling. Research and Development in Knowledge Discovery and Data Mining, Second Pacific-Asia Conference (PAKDD-98), 1998, 1394

MATLAB Release Compatibility
Created with R2019a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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