Abstract
Classification and association rule mining algorithms are two important aspects of data mining. Class association rule mining algorithm is a promising approach for it involves the use of association rule mining algorithm to discover classification rules. This paper introduces an optimal class association rule mining algorithm known as OCARA. It uses optimal association rule mining algorithm and the rule set is sorted by priority of rules resulting into a more accurate classifier. It outperforms the C4.5, CBA, RMR on UCI eight data sets, which is proved by experimental results.
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Jean Claude, T., Sheng, Y., Chuang, L., Kaia, X. (2009). An Optimal Class Association Rule Algorithm. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_39
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DOI: https://doi.org/10.1007/978-3-642-04962-0_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04961-3
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