Abstract: An embodiment involves obtaining a tabular data set with columns that characterize items relating to behavior of components of a communication network; constructing a frequent-pattern tree, each node being associated with: (i) an item-name for representing an item, (ii) a count of transactions from a root node of the tree to the respective node, and (iii) node-links that refer to other nodes in the tree that represent items having the same item-name; traversing the tree to identify a set of nodes with counts greater than a predefined support threshold; generating, from the nodes, association-rules that are based on antecedent items associated with a target item; reducing the association-rules by (i) removing the association-rules in which the antecedent items thereof are a superset or subset of the antecedent items of a further association-rule, or (ii) combining the association-rules that have antecedent items that are at least partially disjoint and conditionally dependent.

Abstract: An embodiment involves obtaining a tabular data set with columns that characterize items relating to behavior of components of a communication network; constructing a frequent-pattern tree, each node being associated with: (i) an item-name for representing an item, (ii) a count of transactions from a root node of the tree to the respective node, and (iii) node-links that refer to other nodes in the tree that represent items having the same item-name; traversing the tree to identify a set of nodes with counts greater than a predefined support threshold; generating, from the nodes, association-rules that are based on antecedent items associated with a target item; reducing the association-rules by (i) removing the association-rules in which the antecedent items thereof are a superset or subset of the antecedent items of a further association-rule, or (ii) combining the association-rules that have antecedent items that are at least partially disjoint and conditionally dependent.