  In the process of knowledge discovery and representation in large datasets
using formal concept analysis, complexity plays a major role in identifying all
the formal concepts and constructing the concept lattice(digraph of the
concepts). For identifying the formal concepts and constructing the digraph
from the identified concepts in very large datasets, various distributed
algorithms are available in the literature. However, the existing distributed
algorithms are not very well suitable for concept generation because it is an
iterative process. The existing algorithms are implemented using distributed
frameworks like MapReduce and Open MP, these frameworks are not appropriate for
iterative applications. Hence, in this paper we proposed efficient distributed
algorithms for both formal concept generation and concept lattice digraph
construction in large formal contexts using Apache Spark. Various performance
metrics are considered for the evaluation of the proposed work, the results of
the evaluation proves that the proposed algorithms are efficient for concept
generation and lattice graph construction in comparison with the existing
algorithms.
