Projects per year
Abstract
In modelbased clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al. (Stat Comput 26:303324, 2016) are sparse finite mixtures, where the prior distribution on the weight distribution of a mixture with K components is chosen in such a way that a priori the number of clusters in the data is random and is allowed to be smaller than K with high probability. The number of clusters is then inferred a posteriori from the data. The present paper makes the following contributions in the context of sparse finite mixture modelling. First, it is illustrated that the concept of sparse finite mixture is very generic and easily extended to cluster various types of nonGaussian data, in particular discrete data and continuous multivariate data arising from nonGaussian clusters. Second, sparse finite mixtures are compared to Dirichlet process mixtures with respect to their ability to identify the number of clusters. For both model classes, a random hyper prior is considered for the parameters determining the weight distribution. By suitable matching of these priors, it is shown that the choice of this hyper prior is far more influential on the cluster solution than whether a sparse finite mixture or a Dirichlet process mixture is taken into consideration.
Original language  English 

Pages (fromto)  3364 
Journal  Advances in Data Analysis and Classification 
Volume  13 
Issue number  1 
DOIs  
Publication status  Published  2019 
Projects
 1 Finished

Shrinking and Regularizing Finite Mixture Models
FrühwirthSchnatter, S. & MalsinerWalli, G.
1/11/16 → 30/04/22
Project: Research funding