Advancing the PAM Algorithm to Semi-Supervised k-Medoids Clustering
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | 17th International Conference on Similarity Search and Applications (SISAP) |
| MU Faculty or unit | |
| Citation | |
| web | 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 |
| Keywords | semi-supervised clustering;k-medoids;partitioning around medoids;FasterPAM;semi-supervised classification;DISA;LMI |
| Attached files | |
| Description | 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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