Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 119–128 (2020)
Abstract. This paper presents a method of generating linguistic summaries of women's menstrual cycles based on the set of concepts describing various aspects of the cycles. These concepts enable description of menstrual cycles that are readable for humans, but they also provide high-level information that can be used as control input for other data processing actions such as e.g. anomaly detection. The labels signifying these concepts are assigned to cycles by means of multivariate time series analysis. The corresponding algorithm is a subsystem of a bigger solution created as a part of an R\&D project.
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