On the evaluation and optimization of LabeledPAM

J 2026

On the evaluation and optimization of LabeledPAM

JÁNOŠOVÁ, Miriama; Andreas LANG; Petra BUDÍKOVÁ; Erich SCHUBERT; Vlastislav DOHNAL et al.

Základní údaje

Originální název

On the evaluation and optimization of LabeledPAM

Autoři

JÁNOŠOVÁ, Miriama; Andreas LANG; Petra BUDÍKOVÁ; Erich SCHUBERT a Vlastislav DOHNAL

Vydání

Information Systems, 2026, 0306-4379

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.400 v roce 2024

Označené pro přenos do RIV

Ano

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

semi-supervised clustering; k-medoids; partitioning around medoids; FasterPAM; semi-supervised classification

Štítky

clustering, DISA, k-medoid clustering, semi-supervised learning

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 1. 4. 2026 11:04, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

The analysis of complex and weakly labeled data is increasingly popular. Traditional unsupervised clustering aims to uncover interrelated sets of objects based on feature-based similarity. This approach often reaches its limits when dealing with complex multimedia data due to the curse of dimensionality, presenting unique challenges. Semi-supervised clustering, which leverages small amounts of labeled data, has the potential to cope with this problem. In this work, we delve into LabeledPAM, a semi-supervised clustering method, which extends FasterPAM, a state-of-the-art 𝑘-medoids clustering algorithm. Our algorithm is designed for both semi-supervised classification, where labels are assigned to clusters with minimal labeled data, and semi-supervised clustering, where new clusters with unknown labels are identified. We propose an optimization to the original LabeledPAM algorithm that reduces its computational complexity. Additionally, we provide an implementation in Rust, which integrates seamlessly with Python libraries. To assess LabeledPAM’s performance, we empirically evaluate its properties by comparing it against a range of semi-supervised clustering algorithms, including density-based ones. We conduct experiments on a collection of real-world datasets. Our results demonstrate that LabeledPAM achieves competitive clustering quality while maintaining efficiency across various scenarios, showing its versatility for real-world applications.

Návaznosti

GF23-07040K, projekt VaV

Název: Naučené indexy pro podobností hledání

Investor: Grantová agentura ČR, Naučené indexy pro podobností hledání, Lead agentura

MUNI/A/1638/2024, interní kód MU

Název: Umělá inteligence a správa komplexních rozsáhlých dat

Investor: Masarykova univerzita, Umělá inteligence a správa komplexních rozsáhlých dat

Citovat

JÁNOŠOVÁ, Miriama; Andreas LANG; Petra BUDÍKOVÁ; Erich SCHUBERT a Vlastislav DOHNAL. On the evaluation and optimization of LabeledPAM. Information Systems. 2026, roč. 135, č. 102580, s. 1-16. ISSN 0306-4379. Dostupné z: https://doi.org/10.1016/j.is.2025.102580.

@article{2511880,
   author = {Jánošová, Miriama and Lang, Andreas and Budíková, Petra and Schubert, Erich and Dohnal, Vlastislav},
   article_number = {102580},
   doi = {https://doi.org/10.1016/j.is.2025.102580},
   keywords = {semi-supervised clustering; k-medoids; partitioning around medoids; FasterPAM; semi-supervised classification},
   language = {eng},
   issn = {0306-4379},
   journal = {Information Systems},
   title = {On the evaluation and optimization of LabeledPAM},
   url = {https://www.sciencedirect.com/science/article/pii/S030643792500064X},
   volume = {135},
   year = {2026}
}

TY  - JOUR
ID  - 2511880
AU  - Jánošová, Miriama - Lang, Andreas - Budíková, Petra - Schubert, Erich - Dohnal, Vlastislav
PY  - 2026
TI  - On the evaluation and optimization of LabeledPAM
JF  - Information Systems
VL  - 135
IS  - 102580
SP  - 1-16
EP  - 1-16
SN  - 03064379
KW  - semi-supervised clustering
KW  - k-medoids
KW  - partitioning around medoids
KW  - FasterPAM
KW  - semi-supervised classification
UR  - https://www.sciencedirect.com/science/article/pii/S030643792500064X
N2  - The analysis of complex and weakly labeled data is increasingly popular. Traditional unsupervised clustering aims to uncover interrelated sets of objects based on feature-based similarity. This approach often reaches its limits when dealing with complex multimedia data due to the curse of dimensionality, presenting unique challenges. Semi-supervised clustering, which leverages small amounts of labeled data, has the potential to cope with this problem. In this work, we delve into LabeledPAM, a semi-supervised clustering method, which extends FasterPAM, a state-of-the-art 𝑘-medoids clustering algorithm. Our algorithm is designed for both semi-supervised classification, where labels are assigned to clusters with minimal labeled data, and semi-supervised clustering, where new clusters with unknown labels are identified. We propose an optimization to the original LabeledPAM algorithm that reduces its computational complexity. Additionally, we provide an implementation in Rust, which integrates seamlessly with Python libraries. To assess LabeledPAM’s performance, we empirically evaluate its properties by comparing it against a range of semi-supervised clustering algorithms, including density-based ones. We conduct experiments on a collection of real-world datasets. Our results demonstrate that LabeledPAM achieves competitive clustering quality while maintaining efficiency across various scenarios, showing its versatility for real-world applications.
ER  -

JÁNOŠOVÁ, Miriama; Andreas LANG; Petra BUDÍKOVÁ; Erich SCHUBERT a Vlastislav DOHNAL. On the evaluation and optimization of LabeledPAM. \textit{Information Systems}. 2026, roč.~135, č.~102580, s.~1-16. ISSN~0306-4379. Dostupné z: https://doi.org/10.1016/j.is.2025.102580.

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