1. A method for use in a data processing system to learn behavior of maintenance costs of products and determine maintenance costs of products, using a learning database containing product characteristics, maintenance agreement conditions and actual historical maintenance costs associated therewith, comprising the steps of: receiving by the data processing system a selection of at least one product characteristic stored in said learning database; determining by the data processing system, from said actual historical maintenance costs in the learning database, first maintenance cost estimations C 0 (i) associated with each modality i of said at least one product characteristic and building a first table storing the modalities and said first maintenance cost estimations associated therewith according to the equation C 0 ⁡ ( i ) = C D ⁡ ( i ) ⁢ dxC ⁡ ( i ) d ⁡ [ D ⁡ ( i ) , C ⁡ ( i ) ] ⁢ dyA ⁡ ( i ) d ⁡ [ D ⁡ ( i ) , A ⁡ ( i ) ] + C C ⁡ ( i ) ⁢ dxD ⁡ ( i ) d ⁡ [ D ⁡ ( i ) , C ⁡ ( i ) ] ⁢ dyA ⁡ ( i ) d ⁡ [ D ⁡ ( i ) , A ⁡ ( i ) ] + C A ⁡ ( i ) ⁢ dxC ⁡ ( i ) d ⁡ [ D ⁡ ( i ) , C ⁡ ( i ) ] ⁢ dyD ⁡ ( i ) d ⁡ [ D ⁡ ( i ) , A ⁡ ( i ) ] + C B ⁡ ( i ) ⁢ dxD ⁡ ( i ) d ⁡ [ D ⁡ ( i ) , C ⁡ ( i ) ] ⁢ dyD ⁡ ( i ) d ⁡ [ D ⁡ ( i ) , A ⁡ ( i ) ] wherein dxM(i) is the distance along the x axis between points (Dref, Mref) and M(i), expressed in years, and M(i) is C(i) or D(i); dyM(i) is the distance along the y axis between points (Dref, Mref) and M(i), expressed in miles, and M(i) is C(i) or D(i); d(M(i),N(i)) is the distance between points M(i) and N(i), expressed in (years 2 miles 2 ) 1/2 : Dref and Mref are defined by the user and correspond to reference duration and mileage; A(i), B(i), C(i), and D(i) are representative values of the duration and mileage of the selection of the at least one product characteristic; C A (i), C B (i), C C (i), and C D (i) are maintenance costs estimates computed on the basis of A(i), B(i), C(i), and D(i); receiving by the data processing system a selection of at least one maintenance agreement condition of said learning database; specifying by the data processing system values of said at least one maintenance agreement condition, determining maintenance cost correction coefficients associated with said specified values and building a second table storing said specified values and said maintenance cost correction coefficients associated therewith, the maintenance cost correction coefficients being determined based upon actual historical maintenance costs for lease durations and mileages within a user defined window surrounding the specified values, the user defined window being selected to include a user specified window allocation distribution of actual historical maintenance costs associated with the modality i of the at least one product characteristic in an interval of duration between C(i) and D(i), and in an interval of miles between A(i) and D(i); and estimating by the data processing system a maintenance cost of said product by multiplying a maintenance cost estimation from the first table by a maintenance cost correction coefficient from the second table; wherein said first and second tables characterize maintenance cost behavior of products contained in said learning database.