categories suffice it is not desirable to introduce spurious categories where they provide no further information thus we could choose where n category count nlimit an upper bound for n and w a positive weight parameter for example 3 the score for categorizations with small category counts will be relatively unaffected by this factor and as the category count approaches the limit the score will rapidly be diminished towards 0 it should be noted however that the particular form of the count term presented above is merely exemplary and others having similar characteristics could likewise be used without departing from the spirit and scope of the instant invention lastly the final summation factor biases our choice towards categorizations which quantifiably present more information about the data value using the information entropy measure from information theory this value effectively measures how many bits would be required in an optimal representation of the category value for example one may consdier the problem of breaking a data value into 4 categories if we chose value ranges which exactly divided the population into 4 equal groups p u00bc in each case then the entropy term is 2 this does not yield a surprising result 4 values in 2 bits but if we had another 4 way categorization where the probability of two categories was 0 and the other two was.