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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GF23-07040K" target="_blank" >GF23-07040K: Naučené indexy pro podobností hledání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

17th International Conference on Similarity Search and Applications (SISAP)

ISBN

9783031758225

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

15

Strana od-do

223-237

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Providence, Rhode Island, USA

Datum konání akce

4. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001422992900019