Entropy-weighted medoid shift: An automated clustering algorithm for high-dimensional data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10256276" target="_blank" >RIV/61989100:27730/25:10256276 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1568494624011219" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494624011219</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.asoc.2024.112347" target="_blank" >10.1016/j.asoc.2024.112347</a>
Alternative languages
Result language
angličtina
Original language name
Entropy-weighted medoid shift: An automated clustering algorithm for high-dimensional data
Original language description
Unveiling the intrinsic structure within high-dimensional data presents a significant challenge, particularly when clusters manifest themselves in lower-dimensional subspaces rather than in the full feature space. This complexity is prevalent in real-world datasets, such as text documents and images, which often contain numerous noisy or sparse features. Traditional clustering methods often overlook these latent subspace structures. This paper introduces a novel subspace-based clustering algorithm designed explicitly to address this challenge. Building upon the robust medoid shift framework, we integrate a dimensionality reduction scheme that dynamically projects data onto evolving subspaces determined through entropy-constrained optimization. This approach effectively filters irrelevant information and identifies underlying clusters, optimizing subspace representation while avoiding trivial solutions. Unlike existing methods, our algorithm ensures convergence without necessitating stopping criteria, thereby enabling efficient processing of large datasets. We validate the efficacy of our approach through extensive experiments on synthetic and real-world datasets, demonstrating substantial performance enhancements over state-of-the-art techniques. By explicitly uncovering the underlying subspace structures, our method opens new avenues for effective high-dimensional data clustering and offers valuable insights into complex data environments.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Soft Computing
ISSN
1568-4946
e-ISSN
1872-9681
Volume of the periodical
169
Issue of the periodical within the volume
Volume 169
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
1-18
UT code for WoS article
001387829800001
EID of the result in the Scopus database
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