Name
Abstract | ||
|---|---|---|
Uncertainty management is necessary for real world applications, especially those used with data mining. The Region Connection Calculus (RCC) and egg-yolk methods have proven useful for the representation of vague regions in spatial data. Rough set theory has been shown to be an effective tool for data mining and for uncertainty management in databases. In this study we use a rough set foundation for expressing topological relationships previously defined for the RCC and egg-yolk methods and show that rough sets can improve on the representation of topological relationships and concepts defined with the other models, which leads to improved mining of spatial data. Finally, we provide an extension of spatial association rule generation that will be able to use rough set–modeled spatial data. © 2004 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1002/int.v19:7 | Int. J. Intell. Syst. |
Keywords | Field | DocType |
data mining,rough set,spatial data | Spatial analysis,Geographic information system,Data modeling,Data mining,Computer science,Rough set,Association rule learning,Artificial intelligence,Dominance-based rough set approach,Machine learning,Spatial database,Region connection calculus | Journal |
Volume | Issue | ISSN |
19 | 7 | 0884-8173 |
Citations | PageRank | References |
15 | 1.02 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Theresa Beaubouef | 1 | 317 | 32.74 |
| Roy Ladner | 2 | 87 | 9.46 |
| Frederick E. Petry | 3 | 562 | 69.24 |