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In this paper, we propose a new text categorization framework based on Concepts Lattice and
cellular automata. In this framework, concept structure are modeled by a Cellular Automaton
for Symbolic Induction (CASI). Our objective is to reduce time categorization caused by the
Concept Lattice. We examine, by experiments the performance of the proposed approach and
compare it with other algorithms such as Naive Bayes and k nearest neighbors. The results
show performance improvement while reducing time categorization.