Name
Abstract | ||
|---|---|---|
Concept lattices are mathematical structures useful for many tasks in knowledge discovery and management. A concept lattice is basically obtained from binary data encoding the membership of some attributes to some objects. Dealing with complex data brings the important problem of discretization and the associated loss of information. To avoid discretization, (i) pattern structures and (ii) symbolic data analysis provide means to analyze such complex data directly.We compare both these approaches and show how they are mutually beneficial. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21881-1_31 | RSFDGrC |
Keywords | Field | DocType |
associated loss,pattern structure,mathematical structure,knowledge discovery,important problem,binary data,complex data,symbolic data analysis,symbolic galois lattice,concept lattice | Discretization,Mathematical structure,Computer science,Complex data type,Theoretical computer science,Symbolic data analysis,Knowledge extraction,Binary data,Formal concept analysis,Encoding (memory) | Conference |
Volume | ISSN | Citations |
6743 | 0302-9743 | 2 |
PageRank | References | Authors |
0.36 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Agarwal | 1 | 2 | 0.36 |
| Mehdi Kaytoue | 2 | 393 | 36.08 |
| Sergei O. Kuznetsov | 3 | 1630 | 121.46 |
| Amedeo Napoli | 4 | 1180 | 135.52 |
| G. Polaillon | 5 | 2 | 0.36 |