Abstract
The simultaneous analysis of quantitative and qualitative variables is not an easy task in general. When a linear model is appropriate, the Generalized Linear Models are commonly used with success. But when the intrinsic structure of the data is not at all linear, they give very poor and confusing results. In this paper, we extensively study how to use the (non linear) Kohonen maps to solve some of the interesting problems which are encountered in data analysis: how to realize a rapid and robust classification based on the quantitative variables, how to visualize the classes, their differences and homogeneity, how to cross the classification with the remaining qualitative variables to interpret the classification and put in evidence the most important explanatory variables.
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Cottrell, M., Rousset, P. (1997). The Kohonen algorithm: A powerful tool for analysing and representing multidimensional quantitative and qualitative data. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032546
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DOI: https://doi.org/10.1007/BFb0032546
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