Vector-based Attribute Reduction Method for Formal Contexts

Attribute reduction is one basic issue in knowledge discovery of information systems. In this paper, based on the object oriented concept lattice and classical concept lattice, the approach of attribute reduction for formal contexts is investigated. We consider attribute reduction and attribute characteristics from the perspective of linear dependence of vectors. We first introduce the notion of context matrix and the operations of corresponding column vectors, then present some judgment theorems of attribute reduction for formal contexts. Furthermore, we propose a new method to reducing formal context and show corresponding reduction algorithms. Compared with previous reduction approaches which employ discernibility matrix and discernibility function to determine all reducts, the proposed approach is more simpler and easier to implement.