A tool developed in Matlab for multiple correspondence analysis of fuzzy coded data sets: application to morphometric skull data.
Multiple Correspondence factorial Analysis is a multivariate method for the exploratory study of multidimensional contingency tables. Its use can be extended to the analysis of a table of fuzzy coded data resulting from a distribution into fuzzy windows defined by linguistic properties. There are few existing software tools that allow performing this type of analysis on a data table; furthermore these tools are not interactive and do not allow defining and representing fuzzy windowing. This paper presents a software tool, developed with Matlab, to compute and represent results from multiple correspondence factorial analyses. Pre-defined membership functions can be selected by the user according to the distribution histograms of the data. This paper presents an application example of this program onto a data table of morphometric parameters of 150 male skulls throughout 5 periods of Egyptian civilization. The results are compared to those of a principal component analysis, which is more often used for the study of experimental data. Our program allows a rapid description of the morphological evolution of skulls over time, notably thanks to a linguistic description of each variable, whereas the results of the latter method are less obvious to observe and require a deeper analysis in order to arrive at the same conclusions.
Pinti
Antonio
A
LAMIH UMR CNRS 8530, Université de Valenciennes, Le Mont Houy, 59313 Valenciennes Cedex 9, France. antonio.pinti@univ-valenciennes.fr
Rambaud
Fabienne
F
Griffon
Jean-Louis
JL
Ahmed
Abdelmalik Taleb
AT
D003627:Data Interpretation, Statistical; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008962:Models, Theoretical; D025341:Principal Component Analysis; D012886:Skull / Q000033:anatomy & histology*; D012984:Software