An Incremental Concept Formation Approach for Learning from Databases

Godin, R. and R. Missaoui, An incremental concept formation approach for learning from databases, Theoretical Computer Science 133 (1994) 3533385. This paper describes a concept formation approach to the discovery of new concepts and implication rules from data. This machine learning approach is based on the Galois lattice theory, and starts from a binary relation between a set of objects and a set of properties (descriptors) to build a concept lattice and a set of rules. Each node (concept) of the lattice represents a subset of objects with their common properties. In this paper, some efficient algorithms for generating concepts and rules are presented. The rules are either in conjunctive or disjunctive form. To avoid the repetitive process of constructing the concept lattice and determining the set of implication rules from scratch each time a new object is introduced in the input relation, we propose an algorithm for incrementally updating both the lattice and the set of generated ...

• Type: Conference
• Year: 1992
• Where: DBWORKSHOPS
• Authors: Rokia Missaoui, Robert Godin
• Keywords: Concept Formation Approach, Concept Lattice, Database, DBWORKSHOPS 1992, Implication Rules