Abstract
This research empirically investigates the performance of conventional rule interestingness measures and discusses their availability to supporting KDD through system-human interaction in medical domain. We compared the evaluation results by a medical expert and that by selected measures for the rules discovered from a dataset on hepatitis. Recall and 2 Measure 1 demon-strated the highest performance, and all measures showed different trends under our experimental conditions. These results indicated that some measures can predict really interesting rules at a certain level and that their combinational use in system-human interaction will be useful.
| Original language | English |
|---|---|
| Pages (from-to) | 1072-1081 |
| Number of pages | 10 |
| Journal | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| Volume | 3029 |
| Publication status | Published - 2004 Dec 9 |
| Externally published | Yes |
| Event | 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada Duration: 2004 May 17 → 2004 May 20 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
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Cite this
Ohsaki, M., Sato, Y., Kitaguchi, S., Yokoi, H., & Yamaguchi, T. (2004). Comparison between objective interestingness measures and real human interest in medical data mining. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3029, 1072-1081.