Name
Abstract | ||
|---|---|---|
An inductive probabilistic approach to formal concept analysis (FCA) is proposed in which probability on formal contexts is considered; probabilistic formal concepts that have predictive force are defined: nonclassified objects can be assigned to earlier found probabilistic formal concepts; random attributes are eliminated from probabilistic formal concepts; probabilistic formal concepts are robust with respect to data noise. A result of experiment is presented in which formal concepts (in their standard definition in FCA) are first distorted by random noise and then recovered by detecting probabilistic formal concepts. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1134/S0361768812050076 | Programming and Computer Software |
Keywords | Field | DocType |
Probability Model,Association Rule,Formal Concept,Formal Context,Impli Cation | Formal system,Computer science,Random noise,Probabilistic CTL,Theoretical computer science,Association rule learning,Probabilistic logic,Probabilistic relevance model,Formal concept analysis,Data Noise | Journal |
Volume | Issue | ISSN |
38 | 5 | 0361-7688 |
Citations | PageRank | References |
3 | 0.42 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. E. Vityaev | 1 | 23 | 4.30 |
| Alexander Demin | 2 | 5 | 0.82 |
| Denis Ponomaryov | 3 | 29 | 7.43 |