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This paper presents CSar, a Michigan-style Learning Classifier System which has been designed for extracting quantitative association rules from streams of unlabeled examples. The main novelty of CSar with respect to the existing association rule miners isthat it evolves the knowledge on-line and so it is prepared to adapt its knowledge to changes in the variable associations hidden in the stream of unlabeled data quickly and efficiently. Preliminary results provided in this paper show that CSar is able to evolve interesting rules on problems that consist of both categorical and continuous attributes. Moreover, the comparison of CSar with Apriori on a problem that consists only of categorical attributes highlights the competitiveness of CSar with respect to more specific learners that perform enumeration to return all possible association rules. These promising results encourage us for further investigating on CSar.