Name
Abstract | ||
|---|---|---|
In the paper we present a new type of attribute reducts in a decision system, which is called parallel reduct. The parallel reduct is the extension of both Pawlak reduct and dynamic reduct. It could be counted by parallel computation, and could be applied to tremendously large data and increase data just like dynamic reducts, but parallel reducts could be got easier than dynamic reducts. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CSO.2009.250 | CSO (2) |
Keywords | Field | DocType |
dynamic reduct,parallel reducts,large data,pawlak reduct,parallel reduct,increase data,dynamic reducts,decision system,parallel computation,decision subsystems,attribute reducts,distributed algorithms,data models,parallel algorithms,rough set theory,data mining,set theory,distributed computing,concurrent computing,parallel computer,rough sets,voting,mathematical model,rough set,mathematics,information systems | Data modeling,Set theory,Reduct,Computer science,Parallel algorithm,Decision system,Theoretical computer science,Rough set,Distributed algorithm,Concurrent computing | Conference |
Citations | PageRank | References |
7 | 0.70 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dayong Deng | 1 | 57 | 7.16 |
| Ji-yi Wang | 2 | 17 | 8.05 |
| Xiangjun Li | 3 | 7 | 1.37 |