1. A computer-based method of generating association rules to extract useful information from an input data set of items for use in market analysis applications, comprising: identifying one or more generic items in the input data set; transforming the input data set into a second data set by mapping each identified generic item into a set of items based on attributes corresponding to the generic item; generating a preliminary set of association rules based on the second data set, where each association rule in the preliminary set of association rules comprises data that represents a plurality of physical or tangible objects as well as an association relationship therebetween; transforming the preliminary set of association rules into a final set of association rules with a processor by: identifying a first rule having a first confidence measure from the preliminary set of association rules; calculating a scaled lift threshold value based on the first confidence measure, where the scaled lift threshold value is scaled as a function of the first confidence measure so that the scaled lift threshold value is larger for a relatively small first confidence measure and is smaller for a relatively large first confidence measure; and removing a second rule having a second confidence measure from the preliminary set of association rules to generate the final set of association rules if the second rule is subsumed by the first rule and the second confidence measure does not exceed the first confidence measure by more than the scaled lift threshold value; and storing the final set of association rules in a memory storage database.