Name
Abstract | ||
|---|---|---|
Mineralization is part of the geologic information, which makes it crucial to mine the geological anomaly from large numbers of data obtained from geologic exploration. Quantification of uncertainty in metallogenic prognosis is an important process to support decision making in mineral exploration. Degree of uncertainty can identify level of quality in the prediction. Rough Set can directly aim at the description set of the given problems, confirm the approximation region by using in discernibility relation to find the inherent rules of these problems. Based on geological and metallogenic regulation, the study makes use of mathematical tools and computer technique to achieve the following aspects: (1) Analyzing the inherent relation between metallization information and metallogenic probability. (2) Revealing the hidden rules and relation behind the data. (3) Choosing the geological anomaly successfully. (4) Establishing the study model of mineralization based on Rough Sets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FSKD.2009.659 | FSKD (1) |
Keywords | Field | DocType |
data mining,mineral exploration,rough set | Data mining,Computer science,Approximation theory,Rough set,Mineral exploration,Artificial intelligence,Machine learning | Conference |
Volume | Issue | Citations |
1 | null | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanbin Yuan | 1 | 140 | 17.67 |
| Liang Xiao | 2 | 5 | 2.72 |
| Jiejun Huang | 3 | 4 | 3.12 |
| Zhang Fan | 4 | 0 | 0.34 |