Abstract
This paper presents the APRIORI-C algorithm, modifying the association rule learner APRIORI to learn classification rules. The algorithm achieves decreased time and space complexity, while still performing exhaustive search of the rule space. Other APRIORI-C improvements include feature subset selection and rule post-processing, leading to increased understandability of rules and increased accuracy in domains with unbalanced class distributions. In comparison with learners which use the covering approach, APRIORI-C is better suited for knowledge discovery since each APRIORI-C rule has high support and confidence.
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Jovanoski, V., Lavrač, N. (2001). Classification Rule Learning with APRIORI-C. In: Brazdil, P., Jorge, A. (eds) Progress in Artificial Intelligence. EPIA 2001. Lecture Notes in Computer Science(), vol 2258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45329-6_8
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DOI: https://doi.org/10.1007/3-540-45329-6_8
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