1. A computerized data mining method for automatically determining a prediction model for a dependent data mining variable based on at least one independent data mining variable, said method comprising the following steps: a variable replacement step ( 103 ) replacing said independent data mining variable with potential values from a global range by a multitude of independent local data mining variables, each independent local data mining variable with potential values from a subrange of said global range; an initialization step ( 104 ) initializing a current prediction model; a looping sequence ( 105 - 108 ) including a first step ( 106 ) having substeps of determining for every independent local data mining variable not yet reflected in said current prediction model a multitude of partial regression functions, each partial regression function depending only on one of said independent local data mining variables; determining for each of said partial regression functions a significance value; selecting the most significant partial regression function and the corresponding not yet reflected local data mining variable; and a second step ( 107 ) of adding said most significant partial regression function to said current prediction model and of associating said corresponding local data mining variable with said significance value.