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April 1991. [10] M. Kifer, G. Lausen, and J. Wu. Logical foundations of object-oriented and frame-based languages. Technical Report 90/14, Department of Computer Science, SUNY at Stony Brook, July 1990. To appear in J. of ACM. [11] S. S. Warren. A logic-based language for database updates. In J. Minker, editor, Foundations of Deductive Databases and Logic Programming, pages 363-394. Morgan-Kaufmann, Los Altos, CA, 1988. M. J. Hayes. Some philosophical problems from the standpoint of artificial intelligence.

11] F. on Knowledge and Data Engineering, 3(2), June 1991, 245-251. [12] F. Sadri, Modeling Uncertainty in Databases, IEEE Int. Conference on Data Engineering, May 1991, 122-131. [13] F. Sadri, Integrity Constraints in the Information Source Tracking Method, IEEE Trans. on Knowledge and Data Engineering, to appear. [14] F. Sadri, Information Source Tracking, Manuscript, September 1991. Submitted for publication. An Incremental Concept Formation Approach for Learning from Databases Rokia Missaoui and Robert Godin Departement de Mathematiques et d'Informatique, UQAM, Montreal, Canada Abstract Based on the Galois lattice theory, we propose a concept formation approach to discover new concepts and implication rules from data.

In the next section we give a background on the concept lattice theory and its relationship with machine learning techniques. Section 3 gives definitions for implications rules. Algorithms for rule generation are presented in Section 4. Finally, a brief discussion on further refinements is proposed. ) describing a set 0 of objects, a set V of properties and a binary relation 'R. 1) between 0 and V, there is a unique ordered set which describes the inherent lattice structure defining natural groupings and relationships among the objects and their properties (Figure 1).

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