In this paper, we propose a new Association Rule Mining algorithm for Classification (ARMC). Our algorithm extracts the set of rules, specific to each class, using a fuzzy approach to select the items and does not require the user to provide thresholds. ARMC is experimentaly evaluated and compared to state of the art classification algorithms, namely CBA, PART and RIPPER. Results of experiments on standard UCI benchmarks show that our algorithm outperforms the above mentionned approaches in terms of mean accuracy. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Zemirline, A., Lecornu, L., Solaiman, B., & Ech-Cherif, A. (2008). An efficient association rule mining algorithm for classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5097 LNAI, pp. 717–728). https://doi.org/10.1007/978-3-540-69731-2_69
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