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Position Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 36

Large Minded Reasoners for Soft and Hard Cluster Validation -- Some Directions

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DOI: http://dx.doi.org/10.15439/2023F7902

Citation: Position Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 36, pages 1–8 ()

Full text

Abstract. In recent research, validation methods for soft and hard clustering through general granular rough clusters are proposed by the first author. Large-minded reasoners are introduced and studied in the context of new concepts of non-stochastic rough randomness in a separate paper by her. In this research, the methodologies are reviewed and new low-cost scalable methodologies and algorithms are invented for computing granular rough approximations of soft clusters for many classes of partially ordered datasets. Specifically, these are applicable to datasets in which attribute values are numeric, vector valued, lattice-ordered or partially ordered. Additionally, new research directions are indicated.

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