LAUSR

Formal concept analysis has been witnessed to be an effective soft computing methodology for data analysis, rule extraction and clustering, but how to build a formal concept lattice efficiently is always a challenge issue. Recent years, several deformation formal contexts are emerging for addressing current important research problems, such as virtual machines scheduling in mobile cloud computing and topological structure analysis in social networks. In this paper, we focus on the symmetry formal context where the objects and attributes are identical, and its formal concept lattice construction. Firstly, we explore the properties of symmetry formal context and discover a symmetry line which must appear in the expected formal concept lattice. Further, a fast algorithm for building concept lattice for symmetry formal context is presented. Then, five social network datasets are utilized for evaluating our proposed algorithm. Experimental results show that our algorithm can speed up 1.2 times and 3.45 times compared to the concept-matrix based concepts generation algorithm and incremental algorithm, respectively.