Concept lattice reduction using different subset of attributes as information granules

In recent years, the output of formal concept analysis has been widely spread in various research fields for knowledge processing tasks. In this process, a major issues arises when large number of formal concepts are generated from the given context. Available approaches lacks in user required dynamic reduction of concept lattice based on shape and size of the given problem. To overcome this problem, the current paper proposes a method to control the size of concept lattice based on user defined subset of attributes (or objects). Further the proposed method provides a way to select some of the important concepts generated from chosen subset of attributes. For this purpose properties of Shannon entropy is utilized by the proposed method to select some of the important concepts at different granulation of their computed weight. The analysis derived from the proposed method is also compared with recently published granulation tree method with an empirical analysis.