A k-anonymous rule induction method for binary classification data

Abstract

In this paper, we investigate a method for inducing k-anonymous rules from a data table about a binary classification toward the rule publication. Although imprecise rules work well for inducing k-anonymous rules from data tables about multiclass classifications, they cannot be applied directly to binary classification data because imprecise rules for binary classification become trivial. The method of subdividing classes has been investigated. However, the method requires a great computational effort caused by plentifully generated subclasses. We proposed an efficient method for inducing k-anonymous rules from a data table about a binary classification. In the proposed method, after the application of a usual induction method, we concentrate on k-anonymous rule induction covering instances which have not yet covered by any k-anonymous rules. The effectiveness of the proposed method is examined by numerical experiments.