Abstract
Frequency-based mining of association rules sometimes suffers rule quality problems. In this paper, we introduce a new measure called surprisal that estimates the informativeness of transactional instances and attributes. We eliminate noisy and uninformative data using the surprisal first, and then generate association rules of good quality. Experimental results show that the surprisal-based pruning improves quality of association rules in question item response datasets significantly.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kim, H., Kwak, EY. (2005). Information-Based Pruning for Interesting Association Rule Mining in the Item Response Dataset. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_54
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DOI: https://doi.org/10.1007/11552413_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28894-7
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