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The processing of dense contexts is a common problem in Formal Concept Analysis. From input contexts, all possible combinations must be evaluated in order to obtain all correlations between objects and attributes. The state-of-the-art shows that this problem can be solved through distributed processing. Partial concepts would be obtained from a distributed environment composed of machine clusters in order to achieve the final set of concepts. Therefore, the goal of this paper is to propose, develop, and evaluate a distributed algorithm with high performance to solve the problem of dense contexts. The speedup achieved through the distributed algorithm shows an improvement of performance, but mainly, a high-balance workload which reduces the processing time considerably. For this reason, the main contribution of this paper is the distributed algorithm, capable of accelerating the processing for dense formal contexts.

A DISTRIBUTED ALGORITHM FOR FORMAL CONCEPTS PROCESSING BASED ON SEARCH SUBSPACES