A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixture model clustering for mixed data with missing information
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Robust mixture modelling using multivariate t-distribution with missing information
Pattern Recognition Letters
Journal of Multivariate Analysis
A genetic algorithm for cluster analysis
Intelligent Data Analysis
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Retail clients latent segments
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
HMM-based hybrid meta-clustering ensemble for temporal data
Knowledge-Based Systems
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The estimation of mixture models has been proposed for quite some time as an approach for cluster analysis. Several variants of the Expectation-Maximization algorithm are currently available for this purpose. Estimation of mixture models simultaneously allows the determination of the number of clusters and yields distributional parameters for clustering base variables. There are several information criteria that help to support the selection of a particular model or clustering structure. However, a question remains concerning the selection of specific criteria that may be more suitable for particular applications. In the present work we analyze the relationship between the performance of information criteria and the type of measurement of clustering variables. In order to study this relationship we perform the analysis of forty-two data sets with known clustering structure and with clustering variables that are categorical, continuous and mixed type. We then compare eleven information-based criteria in their ability to recover the data sets' clustering structures. As a result, we select AIC3, BIC and ICL-BIC criteria as the best candidates for model selection that refers to models with categorical, continuous and mixed type clustering variables, respectively.