Standard
Ensemble Clustering for Boundary Detection in High-Dimensional Data. / Anagnostou, Panagiotis
; Pavlidis, Nicos G.; Tasoulis, Sotiris.
Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Revised Selected Papers. ed. / Giuseppe Nicosia; Varun Ojha; Emanuele La Malfa; Gabriele La Malfa; Panos M. Pardalos; Renato Umeton. Cham: Springer, 2024. p. 324-333 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14506 LNCS).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Anagnostou, P
, Pavlidis, NG & Tasoulis, S 2024,
Ensemble Clustering for Boundary Detection in High-Dimensional Data. in G Nicosia, V Ojha, E La Malfa, G La Malfa, PM Pardalos & R Umeton (eds),
Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14506 LNCS, Springer, Cham, pp. 324-333, 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, Grasmere, United Kingdom,
22/09/23.
https://doi.org/10.1007/978-3-031-53966-4_24APA
Anagnostou, P.
, Pavlidis, N. G., & Tasoulis, S. (2024).
Ensemble Clustering for Boundary Detection in High-Dimensional Data. In G. Nicosia, V. Ojha, E. La Malfa, G. La Malfa, P. M. Pardalos, & R. Umeton (Eds.),
Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Revised Selected Papers (pp. 324-333). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14506 LNCS). Springer.
https://doi.org/10.1007/978-3-031-53966-4_24Vancouver
Anagnostou P
, Pavlidis NG, Tasoulis S.
Ensemble Clustering for Boundary Detection in High-Dimensional Data. In Nicosia G, Ojha V, La Malfa E, La Malfa G, Pardalos PM, Umeton R, editors, Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Revised Selected Papers. Cham: Springer. 2024. p. 324-333. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-53966-4_24
Author
Anagnostou, Panagiotis
; Pavlidis, Nicos G. ; Tasoulis, Sotiris. /
Ensemble Clustering for Boundary Detection in High-Dimensional Data. Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Revised Selected Papers. editor / Giuseppe Nicosia ; Varun Ojha ; Emanuele La Malfa ; Gabriele La Malfa ; Panos M. Pardalos ; Renato Umeton. Cham : Springer, 2024. pp. 324-333 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Bibtex
@inproceedings{3d654d7f32894d96898e8c9f2baff4f6,
title = "Ensemble Clustering for Boundary Detection in High-Dimensional Data",
abstract = "The emergence of novel data collection methods has led to the accumulation of vast amounts of unlabelled data. Discovering well separated groups of data samples through clustering is a critical but challenging task. In recent years various techniques to detect isolated and boundary points have been developed. In this work, we propose a clustering methodology that enables us to discover boundary data effectively, discriminating them from outliers. The proposed methodology utilizes a well established density based clustering method designed for high dimensional data, to develop a new ensemble scheme. The experimental results demonstrate very good performance, indicating that the approach has the potential to be used in diverse domains.",
keywords = "Boundary Data, Ensemble Clustering, Minimum Density Hyperplanes",
author = "Panagiotis Anagnostou and Pavlidis, {Nicos G.} and Sotiris Tasoulis",
year = "2024",
month = feb,
day = "15",
doi = "10.1007/978-3-031-53966-4_24",
language = "English",
isbn = "9783031539657",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "324--333",
editor = "Giuseppe Nicosia and Varun Ojha and {La Malfa}, Emanuele and {La Malfa}, Gabriele and Pardalos, {Panos M.} and Renato Umeton",
booktitle = "Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Revised Selected Papers",
note = "9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023 ; Conference date: 22-09-2023 Through 26-09-2023",
}
RIS
TY - GEN
T1 - Ensemble Clustering for Boundary Detection in High-Dimensional Data
AU - Anagnostou, Panagiotis
AU - Pavlidis, Nicos G.
AU - Tasoulis, Sotiris
PY - 2024/2/15
Y1 - 2024/2/15
N2 - The emergence of novel data collection methods has led to the accumulation of vast amounts of unlabelled data. Discovering well separated groups of data samples through clustering is a critical but challenging task. In recent years various techniques to detect isolated and boundary points have been developed. In this work, we propose a clustering methodology that enables us to discover boundary data effectively, discriminating them from outliers. The proposed methodology utilizes a well established density based clustering method designed for high dimensional data, to develop a new ensemble scheme. The experimental results demonstrate very good performance, indicating that the approach has the potential to be used in diverse domains.
AB - The emergence of novel data collection methods has led to the accumulation of vast amounts of unlabelled data. Discovering well separated groups of data samples through clustering is a critical but challenging task. In recent years various techniques to detect isolated and boundary points have been developed. In this work, we propose a clustering methodology that enables us to discover boundary data effectively, discriminating them from outliers. The proposed methodology utilizes a well established density based clustering method designed for high dimensional data, to develop a new ensemble scheme. The experimental results demonstrate very good performance, indicating that the approach has the potential to be used in diverse domains.
KW - Boundary Data
KW - Ensemble Clustering
KW - Minimum Density Hyperplanes
U2 - 10.1007/978-3-031-53966-4_24
DO - 10.1007/978-3-031-53966-4_24
M3 - Conference contribution/Paper
AN - SCOPUS:85186267375
SN - 9783031539657
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 324
EP - 333
BT - Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Revised Selected Papers
A2 - Nicosia, Giuseppe
A2 - Ojha, Varun
A2 - La Malfa, Emanuele
A2 - La Malfa, Gabriele
A2 - Pardalos, Panos M.
A2 - Umeton, Renato
PB - Springer
CY - Cham
T2 - 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023
Y2 - 22 September 2023 through 26 September 2023
ER -
