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The quality of single classification rules for numerical data can be evaluated by different measures. Common measures are the frequency and the confidence of rules beside others. A problem with these measures is that they are valid for a rule only if an uniform distribution of the data, corresponding to the rule, is assumed. Since this is usually not the case, especially when considering high dimensional data, subrules and their properties should be considered additionally. The frequency and the confidence values of the subrules, summarized in a diagram, give more information about the quality of the rules than the properties of the rules solely.