Abstract: Identifying relations between items in transactions. Local conditional frequency pattern trees are generated in processing nodes in a computer system. The nodes in the local conditional frequency pattern trees represent items and a frequency of the items in the transactions. Global conditional frequency pattern trees are generated in the processing nodes. Each processing node in the processing nodes generates a global conditional frequency pattern tree using a set of local conditional frequency pattern trees in the processing node. The global conditional frequency pattern trees generated by the processing nodes are distributed such that each processing node in the processing nodes has the global conditional frequency pattern trees generated by other processing nodes. Patterns are generated by the computer system. A set of rules is generated using the patterns and the global conditional frequency pattern trees in the processing nodes, wherein the set of rules define the relations between the items.

Abstract: Identifying relations between items in transactions. Local conditional frequency pattern trees are generated in processing nodes in a computer system. The nodes in the local conditional frequency pattern trees represent items and a frequency of the items in the transactions. Global conditional frequency pattern trees are generated in the processing nodes. Each processing node in the processing nodes generates a global conditional frequency pattern tree using a set of local conditional frequency pattern trees in the processing node. The global conditional frequency pattern trees generated by the processing nodes are distributed such that each processing node in the processing nodes has the global conditional frequency pattern trees generated by other processing nodes. Patterns are generated by the computer system. A set of rules is generated using the patterns and the global conditional frequency pattern trees in the processing nodes, wherein the set of rules define the relations between the items.