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This article shows the application of the authors’ own method for visualizing multidimensionality, i.e. so called Pipe of Samples, which makes possible to visualize up to 360 dimensions. This approach constituted the base for development of evolutionary discretisation algorithm dedicated for pre-processing of data to be processed using rough sets theory. The study presents operators of crossing, mutation and selection. Structures of the algorithm data have been prepared on the basis of the aforementioned visualization so that each of the achieved individuals described one complete discretisation solution. Hence, in the proposed approach, the population is a set of many complete discretisations of all the attributes. The solution is optimized by means of evolutionary search for the optimum. The study includes results of experiments that compared LDGen adaptation algorithm with other discretisation methods used in rough sets theory. As main components of the article may be regarded such elements like visualisation method, evolutionary data discretisation method including dedicated operators and discussion on the results of experiments.
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