Methods are described to create more accurate sub sets of user data by introducing dead bands into data clusters. User data is collected and then mined. That produces clusters of data. Dead bands are then generated to delineate and describe the data in the clusters more accurately. This is accomplished by classifying data inside the newly created dead bands as NOT being in either of two or more clusters. For example, three clusters are generated from two. If the two were YES and NO then another set of DON'T KNOW is introduced. The new set improves the precision of choices made using data in the YES and the NO clusters. Dead bands are introduced by establishing a radius from the corners of 2-D shapes containing the clusters or by establishing a horizontal or vertical line in parallel with the edges. Each radius or edge encompasses 80% of user data nearest to the corner or edge of the data set. 20% are outside and excluded from their original set. If lines do not overlap, then a dead-band is created to contain user data that is not as confident. That increases the likelihood of accurate decisions being made about the new sets of user data. Case studies are described to demonstrate that.