Advancing the PAM Algorithm to Semi-Supervised k-Medoids Clustering

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JÁNOŠOVÁ Miriama LANG Andreas BUDÍKOVÁ Petra SCHUBERT Erich DOHNAL Vlastislav

Rok publikování 2024
Druh Článek ve sborníku
Konference 17th International Conference on Similarity Search and Applications (SISAP)
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://link.springer.com/chapter/10.1007/978-3-031-75823-2_19
Doi http://dx.doi.org/10.1007/978-3-031-75823-2_19
Klíčová slova semi-supervised clustering;k-medoids;partitioning around medoids;FasterPAM;semi-supervised classification;DISA;LMI
Přiložené soubory
Popis The analysis of complex, weakly labeled data is increasingly popular, presenting unique challenges. Traditional unsupervised clustering aims to uncover interrelated sets of objects using feature-based similarity of the objects, but this approach often hits its limits for complex multimedia data. Thus, semi-supervised clustering that exploits small amounts of labeled training data has gained traction recently. % In this paper, we propose LabeledPAM, a semi-supervised extension of FasterPAM, a state-of-the-art k-medoids clustering algorithm. Our approach is applicable in semi-supervised classification tasks, where labels are assigned to clusters with minimal labeled data, as well as in semi-supervised clustering scenarios, identifying new clusters with unknown labels. We evaluate our proposal against other semi-supervised clustering techniques suitable for arbitrary distances, demonstrating its efficacy and versatility.
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