Tree-Like Parallelization of Reduct and Construct Computation

The paper addresses the problem of parallel computing in reduct/construct generation. The reducts are subsets of attributes that may be successfully applied in information/decision table analysis. Constructs, defined in a similar way, represent a notion that is a kind of generalization of the reduct. They ensure both discernibility between pairs of objects belonging to different classes (in which they follow the reducts) as well as similarity between pairs of objects belonging to the same class (which is not the case with reducts). Unfortunately, exhaustive sets of minimal constructs, similarly to sets of minimal reducts, are NP-hard to generate. To speed up the computations, decomposing the original task into multiple subtasks and executing these in parallel is employed. The paper presents a so-called constrained tree-like model of parallelization of this task and illustrates practical behaviour of this algorithm in a computational experiment.