Using metarules to organize and group discovered association rules

The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all of the rules, especially because many rules are redundant and contained in other rules. We discuss how the sparseness of the data affects the redundancy and containment between the rules and provide a new methodology for organizing and grouping the association rules with the same consequent. It consists of finding metarules, rules that express the associations between the discovered rules themselves. The information provided by the metarules is used to reorganize and group related rules. It is based only on data-determined relationships between the rules. We demonstrate the suggested approach on actual manufacturing data and show its effectiveness on several benchmark data sets.