The Magnetotelluric (MT) method is a passive geophysical technique used to study the Earth's subsurface by measuring variations in natural electromagnetic fields. From this geophysical method is possible to compute the apparent resistivity data of a multi-layered subsurface, that reflect at each depth, the impact of a sequence of layers with varying resistivities above the measurement point. This Thesis is a prosecution of the work done by C. Hernandez, who described a method for rescaling 1D MT apparent resistivity data into layered subsurface resistivity models using a relationship called depth/pseudo-depth rescaling function. The added value of this study lies in applying clustering algorithms (the three employed are k-means, CURE, and OPTICS) to the apparent resistivity data trying to reduce the number of rescaling functions needed (and therefore the number of resistivity models required through inversion processes) for the rescaling process, using one rescaling function for each cluster.