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A post processing method is described that acts on two-dimensional clusters of data produced from a data mining system. Dead bands are automatically created that further define the clusters. This was achieved by defining data within the dead bands as NOT belonging to either cluster. The three clusters produced were definitely YES, definitely NO and a new set of DON’T KNOW. The creation of the new set improved the accuracy of decisions made about the data remaining in YES and NO clusters. The introduction of the dead bands was achieved by either setting a radius during the learning process or by setting a straight line boundary. Each radius (or line) was calculated during the learning process by considering the two dimensional position of each of the users within each cluster of dimensions. A radius line (or straight line) was then introduced so that the 80% of users within a particular dimension who were nearest to the origin (or edge) were placed into a set. The other 20% were outside the radius line (or straight line) and not recorded as being part of the set. If the two lines did not overlap, then this sometimes created a dead-band that contained users with less certain results and that in turn increased the accuracy of the other sets. Two case studies are presented as examples of that improvement.

Post Processing Method that Acts on Two-dimensional Clusters of User Data to Produce Dead Bands and Improve Classification