and has a support level and accuracy level which is lower than p 0 110 as the method continues it becomes increasingly difficult to find rules that have high support and high accuracy in such cases a preference is given to the support level as illustrated in fig1 d where p 2 112 is preferred over q 0 120 or q 1 121 the method ends when at least a predefined portion of the target class example is supported for example greater than 95 of the positive examples 102 if the method were to continue the additional rules would have lower accuracies to the point where the accuracies would be undesirable if an accuracy threshold is set lower the method may proceed beyond p 2 112 to cover additional positive examples due to the preference for support in later iterations some negative examples 103 may be supported which can be referred to as false positives 131 and 132 these are shown as the shaded area in fig1 e referring to fig2 the method collects all the examples supported by the p rules 212 and learns n rules 214 which remove the false positives from the examples included by the p rules thereby increasing the precision of the p rules referring to fig1 e on a dataset including records supported by the union of the p rules 131 133 the method begins an inverse learning method the new target class is now the absence of original target class the starting dataset 131 133 is shown as a restricted subset of the original dataset 101 from fig1 a the method determines a first n rule n 0 140 each n rule determines and removes false positive examples of the original target class the n rule.