This research empirically investigates the performance of conventional rule interestingness measures and discusses their practicality for supporting KDD through human-system interaction in medical domain. We compared the evaluation results by a medical expert and those by selected measures for the rules discovered from a dataset on hepatitis. Recall, Jaccard, Kappa, CST, χ2-M, and Peculiarity demonstrated the highest performance, and many measures showed a complementary trend under our experimental conditions. These results indicate that some measures can predict really interesting rules at a certain level and that their combinational use will be useful. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Ohsaki, M., Kitaguchi, S., Okamoto, K., Yokoi, H., & Yamaguchi, T. (2004). Evaluation of rule interestingness measures with a clinical dataset on hepatitis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. https://doi.org/10.1007/978-3-540-30116-5_34
Mendeley helps you to discover research relevant for your work.