cls.draw1.5 {mcclust}

Sample of Clusterings from Posterior Distribution of Bayesian Cluster Model

Description

Output of a Dirichlet process mixture model with normal components fitted to the data set Ysim1.5. True clusters are given by rep(1:8,each =50).

Usage

data(cls.draw1.5)

Format

matrix with 500 rows and 400 columns. Each row contains a clustering of the 400 observations.

Source

Fritsch, A. and Ickstadt, K. (2009) An improved criterion for clustering based on the posterior similarity matrix, Bayesian Analysis, accepted.