Global Optimization of Exact Association Rules Relative to Length

In the paper, an application of dynamic programming approach to global optimization of exact association rules relative to length is presented. It is an extension of the dynamic programming approach to optimization of decision rules to inconsistent tables. An information system I is transformed into a set of decision tables {If1 , . . . , Ifn+1}. The algorithm constructs, for each decision table from the set {If1 , . . . , Ifn+1}, a directed acyclic graph ∆(Ifi), i = 1, . . . , n + 1. Based on the graph, the set of so-called irredundant (fi)association rules can be described. The union of sets of (fi)-association rules, i = 1, . . . , n + 1, is considered as a set of association rules for information system I . Then, global optimization relative to length is made and sets of association rules with minimum length, for each row of information system I , are obtained. Preliminary experimental results with data sets from UCI Machine Learning Repository are included.