Multivariate Data Analysis: Theory, Applications and Interpretation Issues

Dear Colleagues,

Data mining covers all forms of qualitative, quantitative, textual, image, and sound data today. The ease of expressing the results of an algorithm in human language is an essential condition for its use. J. P. Benzécri (1976), in L’Analyse des Données, I La Taxonomie-Les Peurs Enfantines, when referring to a classification tree constructed to represent the structure of a dataset related to children’s fears, wrote: 

"4. Validité d’une interprétation verbale. Nous voici parvenus, non-sens peine, à munir d’une légende la figure produite par l’ordinateur. Que vaut cette légende? En quel sens peut-on dire qu’un nom est bien choisi pour une classe? De quelle utilité peut être un tel nom? Le nom doit cerner, définir la classe: il doit à la fois s’accorder avec le contenu de cette classe……”.

With this Special Issue, in addition to targeting new, innovative methods and relevant applications of data analysis, contributions covering solutions for interpretation problems or theoretical constructions that may lead to general formulations of interpretation problems in the context of multivariate data analysis are sought, namely:

• Generalization of biplots for three or more modes to d > 3 dimensions;
• Cluster interpretations;
• Interpretability of algorithms;
• Results meaning and interpretation theory;
• Software: R or Python packages for new algorithms;
• Trees comparison;
• Statistical Modeling.

Dr. Valter Martins Vairinhos
Dr. Luis M. Grilo
Dr. Stelios D. Georgiou
Guest Editors

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• multivariate data analysis
• statistical Modeling
• data mining
• explainability
• graphs and hypergraphs
• graphics
• interpretability
• interpretation theory
• semantics
• text mining
• XAI