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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Hashtag Discernability - Competitiveness Study of Graph Spectral and Other Clustering Methods

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DOI: http://dx.doi.org/10.15439/2023F2398

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 759–767 ()

Full text

Abstract. Spectral clustering methods are claimed to possess ability to represent clusters of diverse shapes, densities etc. They constitute an approximation to graph cuts of various types (plain cuts, normalized cuts, ratio cuts). They are applicable to unweighted and weighted similarity graphs. We perform an evaluation of these capabilities for clustering tasks of increasing complexity.

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