A Generalization of the Least General Generalization
Hiroki Arimura, T Shinohara, Shinya Otsuki, Hiroki Ishizaka · 1994
Abstract In this chapter, we present a polynomial time algorithm, called a k-minimal multiple generalization (k-mmg) algorithm, where k ≥ 1, and its application to inductive learning problems. The algorithm is a natural extension of the least general generalization algorithm developed by Plotkin and Reynolds. Given a finite set of ground first-order terms, the k-mmg algorithm generalizes the examples by at most k first-order terms, while Plotkin’s algorithm does so by a single first-order term. We apply the k-mmg algorithm to several learning problems in inductive logic programming, and knowledge discovery in databases.