Abstract
This paper presents a post-processing algorithm of rule discovery for augmenting the readability of a discovered rule set. Rule discovery, in spite of its usefulness as a fundamental data-mining technique, outputs a huge number of rules. Since usefulness of a discovered rule is judged by human inspection, augmenting the readability of a discovered rule set is an important issue. We formalize this problem as a transformation of a rule set into a tree structure called a visual graph. A novel information-based criterion which represents compressed entropy of a data set per description length of the graph is employed in order to evaluate the readability quantitatively. Experiments with an agricultural data set in cooperation with domain experts confirmed the effectiveness of our method in terms of readability and validness.
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Suzuki, E., Ishihara, H. (1999). Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_50
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DOI: https://doi.org/10.1007/978-3-540-48061-7_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
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