Disjunctive Learning with a Soft-Clustering Method

In the case of concept learning from positive and negative examples, it is rarely possible to find a unique discriminating conjunctive rule; in most cases, a disjunctive description is needed. This problem, known as disjunctive learning, is mainly solved by greedy methods, iteratively adding rules until all positive examples are covered. Each rule is determined by discriminating properties, where the discriminating power is computed from the learning set. Each rule defines a subconcept of concept to be learned with these methods. The final set of sub-concepts is then highly dependent from both the learning set and the learning method. In this paper, we propose a different strategy: we first build clusters of similar examples thus defining subconcepts, and then we characterize each cluster by a unique conjunctive definition. The clustering method relies on a similarity measure designed for examples described in first order logic. The main particularity of our clustering method i...

• Type: Conference
• Year: 2003
• Where: ILP
• Authors: Guillaume Cleuziou, Lionel Martin, Christel Vrain
• Keywords: Discriminating Conjunctive Rule, ILP 2003, Learning Set, Negative Examples