Most ACs read the list of rules in order, and apply the first matching rule to label the new record.
The rules of an AC inherit some of the metrics of association rules, like the support or the confidence. Metrics can be used to order or filter the rules in the model and to evaluate their quality.
The first proposal of a classification model made of association rules was FBM. The approach was popularized by CBA, although other authors had also previously proposed the mining of association rules for classification. Other authors have since then proposed multiple changes to the initial model, like the addition of a redundant rule pruning phase or the exploitation of Emerging Patterns.
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Ali, Kamal; Manganaris, Stefanos; Srikant, Ramakrishnan (1997-08-14). "Partial classification using association rules". KDD'97. AAAI Press: 115–118. {{cite journal}}: Cite journal requires |journal= (help) http://dl.acm.org/citation.cfm?id=3001392.3001412
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Chen, Guoqing; Liu, Hongyan; Yu, Lan; Wei, Qiang; Zhang, Xing (2006). "A new approach to classification based on association rule mining". Decision Support Systems. 42 (2): 674–689. doi:10.1016/j.dss.2005.03.005. ISSN 0167-9236. /wiki/Doi_(identifier)
Wang, Ke; Zhou, Senqiang; He, Yu (2000). "Growing decision trees on support-less association rules". Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. New York, New York, USA: ACM Press. pp. 265–269. CiteSeerX 10.1.1.36.9265. doi:10.1145/347090.347147. ISBN 978-1581132335. S2CID 8296096. 978-1581132335