Efficient Attribute Reduction on Fuzzy Context

As the size of data table grows, the concepts generated become larger in number. In order to make the set of extent unchanged, attribute reduction based on concept lattice focuses on finding out minimum subsets of attributes and make decision problem simplified. This paper mainly studies on attribute reduction based on fuzzy context (L-context), transforms L-context to classic context by setting a threshold, presents reducible element and constitution of attribute reduction set, proposes attribute reduction and discusses the time complexity. Experimental analysis show that our algorithm is more effective with attributes less than objects.