[. References and . Bayoudh, Learning by analogy: A classification rule for binary and nominal data, Proc. 20th Int. Joint Conf. on Artificial Intelligence (IJCAI'07), vol.47, pp.1575-1581, 1993.

R. R. Davies, S. J. Davies, ;. Russell, S. L. Denecke, K. Gust et al., Wismath. Universal Algebra and Applications in Theoretical Computer Science. CRC / C&H, Proceedings of the 30th International Conference on International Conference on Machine Learning, vol.354, pp.141-146, 1982.

;. D. Lau and . Lau, Function Algebras on Finite Sets: Basic Course on Many-Valued Logic and Clone Theory, 2006.

, Springer Monographs in Mathematics, 2006.

;. L. Prade, H. Miclet, . Prade, and . Miclet, Analogical dissimilarity: Definition, algorithms and two experiments in machine learning, Proc. 10th Eur. Conf. on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (EC-SQARU'09), vol.5590, pp.441-505, 2008.

;. S. Russell and . Russell, The Use of Knowledge in Analogy and Induction, 1989.

M. H. Stone, Stroppa and F. Yvon. Analogical learning and formal proportions: Definitions and methodological issues, Trans. of the American Mathematical Society, vol.40, issue.1, pp.37-111, 1936.

;. A. Szendrei and . Szendrei, On the technique of calculating propositions in symbolic logic, Czechoslovak Math. J, vol.30, issue.105, pp.9-28, 1927.