Name
Abstract | ||
|---|---|---|
Computing core attributes is one of key problems of rough set theory. Many researchers proposed lots of algorithms for computing core. Unfortunately, most of them are designed for static databases. However, many real datasets are dynamic. In this paper, a quick incremental updating core algorithm is proposed, which only allies on the updating parts of discernibility matrix and does not need to store, re-compute and re-analyze discernibility matrix, when new objects are added to decision table. Both of theoretical analysis and experimental results show that the algorithm is effective and efficient. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16248-0_38 | RSKT |
Keywords | Field | DocType |
key problem,decision table,computing core attribute,rough set theory,discernibility matrix,re-analyze discernibility matrix,real datasets,new object,core algorithm,rough set | Data mining,Decision table,Computer science,Matrix (mathematics),Algorithm,Rough set,Artificial intelligence,Machine learning,Dominance-based rough set approach | Conference |
Volume | ISSN | ISBN |
6401 | 0302-9743 | 3-642-16247-9 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Ge | 1 | 9 | 4.76 |
| Chuanjian Yang | 2 | 12 | 3.02 |
| Wanlian Yuan | 3 | 0 | 0.34 |