@article{2221,
  author = {Ensheng Zhang},
  title = {A Fast Algorithm for Attribute Reduction of Formal Concept Analysis},
  journal = {Journal of Data Processing },
  year = {2017},
  volume = {7},
  number = {1},
  doi = {},
  url = {http://www.dline.info/jdp/fulltext/v7n1/jdpv7n1_1.pdf},
  abstract = {Formal concept analysis is an effective tools for knowledge discovery, information retrieval, machine learning, software engineering, etc.., 
The attribute reduction of concept lattices can reduce the complexity of concept lattices. Concept lattice is the central notion of a formal 
concept analysis, a new area of research which is based on a set-theoretical model of concepts and conceptual hierarchies. This model yields 
not only a new approach to data analysis, but also methods for formal representation of conceptual knowledge. Several algorithms were proposed 
for the attribute reduction of concept lattices, such as the discernibility attribute matrix method, requiring all the formal concepts in concept 
lattices to be solved, which is also a difficult job. This paper proposes a fast algorithm to work out reduction attribute directly on object set, 
which reduces the complexity and the calculation of concept lattice structuring. },
}