1. A computer implemented web-based predictive modeling method to extract predictive segments from sampled data used for predicting subject response, comprising the steps of: providing segmentation attributes and sampled data; and analyzing the distribution of sampled data; wherein said analysis of the distribution of sampled data comprises the steps of: ordering the transactions and occurrences by dimension and treating each dimension one at a time and independently; creating a cumulative sequence by adding P to the previous value when the next transaction contains the item of interest and subtracting A when the item is not present, such that P=1/N P , where N P is the total number of transactions containing the item of interest, and A=1/N A , where N A is the total number of transactions not containing the item of interest, and where the total number of transactions is N total =N P +N A ; determining the sequence of maximum relative probability of the item wherein the max and the min correspond to the candidate partition points of the dimension; partitioning the dimension using the point furthest from the edge of the domain of the dimension in sample order; and calculating the density factor d=r/s, whereby r=(number of items of interest in peak sequence) and s=(number of all items in peak sequence) and d is a number between 0 and 1; whereby the density factor is considered significant if R=(r−r avg )/√{square root over (r)}≧T, where r avg =s·N P /N total and T is a predetermined user specified significance threshold.