1. A method of determining regression functions from a computer data input, the regression functions for use in data mining, prediction, calibration, segmentation or response analysis, the method using K-Harmonic Means regression clustering and comprising the steps of: selecting K regression functions ƒ 1 ,. .. , ƒ K ; associating an i-th data point from a dataset with a k-th regression function using a soft membership function; providing a weighting to each data point using a weighting function to determine a particular data point's participation in a calculation of a residue error; calculating a residue error between a weighted i-th data point and its associated regression function; iterating to minimize a total residue error; and identifying suitable regression functions for use in the analysis.