Patent ID: 6895411

Claim:
A computerized data mining method for automatically determining a most significant 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 plurality 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 a 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, in said variable replacement step said global range being defined by its center defined by the mean value of training data used for the determination of the most significant prediction model, and by a lower and upper limit with a distance from said center being a predefined multiple of the standard deviation of said training data, and said subranges and said corresponding local data mining variables are defined as a fixed number h of subranges by dividing said global range into h equidistant subranges.