How to improve robustness in Kohonen maps and display additional information in Factorial Analysis: application to text mining

Nicolas Bourgeois (1) , Marie Cottrell (1) , Benjamin Deruelle (2) , Stéphane Lamassé (2) , Patrick Letrémy (1)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
2 LAMOP - Laboratoire de Médiévistique Occidentale de Paris

This article is an extended version of a paper presented in the WSOM'2012 conference [1]. We display a combination of factorial projections, SOM algorithm and graph techniques applied to a text mining problem. The corpus contains 8 medieval manuscripts which were used to teach arithmetic techniques to merchants. Among the techniques for Data Analysis, those used for Lexicometry (such as Factorial Analysis) highlight the discrepancies between manuscripts. The reason for this is that they focus on the deviation from the independence between words and manuscripts. Still, we also want to discover and characterize the common vocabulary among the whole corpus. Using the properties of stochastic Kohonen maps, which define neighborhood between inputs in a non-deterministic way, we highlight the words which seem to play a special role in the vocabulary. We call them fickle and use them to improve both Kohonen map robustness and significance of FCA visualization. Finally we use graph algorithmic to exploit this fickleness for classification of words.

Mots clés: Text mining; Kohonen maps; Factorial Analysis; Middle ages scientific literature; graphs

Domaines: Statistiques [math.ST]

Nicolas Bourgeois, Marie Cottrell, Benjamin Deruelle, Stéphane Lamassé, Patrick Letrémy. How to improve robustness in Kohonen maps and display additional information in Factorial Analysis: application to text mining. Neurocomputing, 2014, 147, pp.120-135. ⟨10.1016/j.neucom.2013.12.057⟩. ⟨hal-01168120⟩