Abstract
Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data. © 2006, Taylor & Francis Group, LLC. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 981-986 |
| Journal | International Journal of Systems Science |
| Volume | 37 |
| Issue number | 13 |
| DOIs | |
| Publication status | Published - 20 Nov 2006 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Data mining
- Fuzzy sets
- Monte Carlo Simulation
- Uncertainty