SwePub - search: L773:1862 5347 OR L773:1862 53...

Enumeration	Reference	Cover	Find
1.	Corander, Jukka, et al. (author) Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy 2009 In: Advances in Data Analysis and Classification. - : Springer Berlin/Heidelberg. - 1862-5347 .- 1862-5355. ; 3:1, s. 3-24 Journal article (peer-reviewed)abstract Advantages of statistical model-based unsupervised classification over heuristic alternatives have been widely demonstrated in the scientific literature. However, the existing model-based approaches are often both conceptually and numerically instable for large and complex data sets. Here we consider a Bayesian model-based method for unsupervised classification of discrete valued vectors, that has certain advantages over standard solutions based on latent class models. Our theoretical formulation defines a posterior probability measure on the space of classification solutions corresponding to stochastic partitions of observed data. To efficiently explore the classification space we use a parallel search strategy based on non-reversible stochastic processes. A decision-theoretic approach is utilized to formalize the inferential process in the context of unsupervised classification. Both real and simulated data sets are used for the illustration of the discussed methods.

Corander, Jukka, et al. (author)
Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy
2009
In: Advances in Data Analysis and Classification. - : Springer Berlin/Heidelberg. - 1862-5347 .- 1862-5355. ; 3:1, s. 3-24
Journal article (peer-reviewed)abstract
- Advantages of statistical model-based unsupervised classification over heuristic alternatives have been widely demonstrated in the scientific literature. However, the existing model-based approaches are often both conceptually and numerically instable for large and complex data sets. Here we consider a Bayesian model-based method for unsupervised classification of discrete valued vectors, that has certain advantages over standard solutions based on latent class models. Our theoretical formulation defines a posterior probability measure on the space of classification solutions corresponding to stochastic partitions of observed data. To efficiently explore the classification space we use a parallel search strategy based on non-reversible stochastic processes. A decision-theoretic approach is utilized to formalize the inferential process in the context of unsupervised classification. Both real and simulated data sets are used for the illustration of the discussed methods.

Träfflista för sökning "L773:1862 5347 OR L773:1862 5355 "