Journal
JOURNAL OF STATISTICAL SOFTWARE
Volume 58, Issue 10, Pages 1-31Publisher
JOURNAL STATISTICAL SOFTWARE
Keywords
EM algorithm; finite mixture; hypergeometric function F-0(1); modified Bessel function ratio; R; von Mises-Fisher distribution
Funding
- Austrian Science Fund (FWF) [V170-N18]
- Austrian Science Fund (FWF) [V170] Funding Source: Austrian Science Fund (FWF)
- Austrian Science Fund (FWF) [V 170] Funding Source: researchfish
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Finite mixtures of von Mises-Fisher distributions allow to apply model-based clustering methods to data which is of standardized length, i.e., all data points lie on the unit sphere. The R package movMF contains functionality to draw samples from finite mixtures of von Mises-Fisher distributions and to fit these models using the expectation-maximization algorithm for maximum likelihood estimation. Special features are the possibility to use sparse matrix representations for the input data, different variants of the expectation-maximization algorithm, different methods for determining the concentration parameters in the M-step and to impose constraints on the concentration parameters over the components. In this paper we describe the main fitting function of the package and illustrate its application. In addition we compare the clustering performance of finite mixtures of von Mises-Fisher distributions to spherical k-means. We also discuss the resolution of several numerical issues which occur for estimating the concentration parameters and for determining the normalizing constant of the von Mises-Fisher distribution.
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