A Permutation-Translation Simulated Annealing Algorithm for L1 and L2 Unidimensional Scaling
Journal of Classification
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
A dual latent class unfolding model for two-way two-mode preference rating data
Computational Statistics & Data Analysis
Cluster Analysis
Optimization Strategies for Two-Mode Partitioning
Journal of Classification
Factorial k-means analysis for two-way data
Computational Statistics & Data Analysis
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
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Classification and spatial methods can be used in conjunction to represent the individual information of similar preferences by means of groups. In the context of latent class models and using Simulated Annealing, the cluster-unfolding model for two-way two-mode preference rating data has been shown to be superior to a two-step approach of first deriving the clusters and then unfolding the classes. However, the high computational cost makes the procedure only suitable for small or medium-sized data sets, and the hypothesis of independent and normally distributed preference data may also be too restrictive in many practical situations. Therefore, an alternating least squares procedure is proposed, in which the individuals and the objects are partitioned into clusters, while at the same time the cluster centers are represented by unfolding. An enhanced Simulated Annealing algorithm in the least squares framework is also proposed in order to address the local optimum problem. Real and artificial data sets are analyzed to illustrate the performance of the model.