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In this paper, we introduce a new approach SEAA to reduce dimensionality of multidimensional data series. The approach creates a nominal (symbolic) representation of the original data series and considerably reduces their dimensionality. Experimental validation of the proposed dimension reduction was carried out for classification and clustering tasks. The calculations have shown that even deployment of large reduction of dimensionality causes the new representations to preserve information about the data series characteristics and retain information sufficient to their proper classification and clustering.
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Projects co-financed by:
Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.