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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00140288" target="_blank" >RIV/00216224:14330/25:00140288 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-75823-2_19" target="_blank" >http://dx.doi.org/10.1007/978-3-031-75823-2_19</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-75823-2_19" target="_blank" >10.1007/978-3-031-75823-2_19</a>

Result language
angličtina
Original language name
Advancing the PAM Algorithm to Semi-Supervised k-Medoids Clustering
Original language 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.
Czech name
—
Czech description
—

Project
<a href="/en/project/GF23-07040K" target="_blank" >GF23-07040K: Learned Indexing for Similarity Searching</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
17th International Conference on Similarity Search and Applications (SISAP)
ISBN
9783031758225
ISSN
0302-9743
e-ISSN
—
Number of pages
15
Pages from-to
223-237
Publisher name
Springer
Place of publication
Cham
Event location
Providence, Rhode Island, USA
Event date
Nov 4, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001422992900019

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