Instance-based prediction of real-valued attributes
Computational Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Genetic fuzzy discretization with adaptive intervals for classification problems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
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Latent Segments Models (LSM) are commonly used as an approach for market segmentation. When using LSM, several criteria are available to determine the number of segments. However, it is not established which criteria are more adequate when dealing with a specific application. Since most market segmentation problems involve the simultaneous use of categorical and continuous base variables, it is particularly useful to select the best criteria when dealing with LSM with mixed type base variables. We first present an empirical test, which provides the ranking of several information criteria for model selection based on ten mixed data sets. As a result, the ICL-BIC, BIC, CAIC and ${\mathcal L}$ criteria are selected as the best performing criteria in the estimation of mixed mixture models. We then present an application concerning a retail chain clients’ segmentation. The best information criteria yield two segments: Preferential Clients and Occasional Clients.