Frequent Itemset Border Approximation by Dualization

Abstract

The approach F IBAD is introduced with the purpose of computing approximate borders of frequent itemsets by leveraging dualization and computation of approximate minimal transversals of hypergraphs. The distinctiveness of the F IBAD's theoretical foundations is the approximate dualization where a new function f is defined to compute the approximate negative border. From a methodological point of view, the function f is implemented by the method AM T HR that consists of a reduction of the hypergraph and a computation of its minimal transversals. For evaluation purposes, we study the sensibility of F IBAD to AM T HR by replacing this latter by two other algorithms that compute approximate minimal transversals. We also compare our approximate dualization-based method with an existing approach that computes directly, without dualization, the approximate borders. The experimental results show that our method outperforms the other methods as it produces borders that have the highest quality.