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Ensemble Clustering with Heterogeneous Transfer Learning Full article

Conference 11th International Conference on Analysis of Images, Social Networks and Texts
28-30 Sep 2023 , Ереван
Source Analysis of Images, Social Networks and Texts : 11th International Conference, AIST 2023, Yerevan, Armenia, September 28–30, 2023, Revised Selected Papers
Compilation, Springer. Switzerland.2024. 364 c. ISBN 978-3-031-54534-4.
Journal Lecture Notes in Computer Science
ISSN: 0302-9743 , E-ISSN: 1611-3349
Output data Year: 2024, Volume: 14486, Pages: 255-266 Pages count : 12 DOI: 10.1007/978-3-031-54534-4_18
Tags Ensemble Clustering · Transfer Learning · Co-association matrix
Authors Berikov Vladimir 1
Affiliations
1 Sobolev Institute of mathematics

Funding (1)

1 Sobolev Institute of Mathematics FWNF-2022-0015

Abstract: This work introduces a novel approach to ensemble clustering by incorporating transfer learning. We address a clustering problem where, in addition to the data being analyzed, we have access to “similar” labeled data. The datasets may have different feature descriptions. Our method revolves around constructing meta-features that capture the structural characteristics of the data and transferring them from the source domain to the target domain. To define meta-features, we use the cluster ensemble method. Through extensive Monte Carlo modeling experiments, we have demonstrated the effectiveness of our proposed method. Notably, compared to other similar approaches, our method exhibits the capability to handle arbitrary feature descriptions in both the source and target domains. Additionally, it offers a reduced computational complexity, making it more efficient in practice.
Cite: Berikov V.
Ensemble Clustering with Heterogeneous Transfer Learning
In compilation Analysis of Images, Social Networks and Texts : 11th International Conference, AIST 2023, Yerevan, Armenia, September 28–30, 2023, Revised Selected Papers. – Springer., 2024. – C.255-266. – ISBN 978-3-031-54534-4. DOI: 10.1007/978-3-031-54534-4_18 Scopus OpenAlex
Dates:
Submitted: Aug 31, 2023
Published print: Mar 12, 2024
Published online: Mar 12, 2024
Identifiers:
Scopus: 2-s2.0-85189499128
OpenAlex: W4392956931
Citing: Пока нет цитирований
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