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Existing classification procedures fail to uncover the predictive structure of classification problems, which is key to taking actions. This work suggests a new approach for finding highly actionable rules, using existing association rules mining algorithms, to explain the occurrence of events in mixed high-dimensional manufacturing data. Solutions to several limitations to association rules mining from process data are addressed and a new methodology for organizing and grouping the association rules with the same consequent is provided. Supervised association rules mining from a heterogeneous data space requires discretizing the continuous attributes. This step should be carried out with a minimum information loss. A discretization algorithm called Random Forests Discretizer is introduced in this work, it derives its ability in conserving the data properties from the Random Forests learning algorithm. Finally, supervised association rules along with their corresponding metarulesare used for clustering in a categorical feature space. This work introduces an algorithm called Supervised Clustering with Association Rules, for clustering massive high dimensional categorical data.