Abstract
Semi-supervised classification is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatically increasing. In this paper, we introduce a unified framework for semi-supervised classification based on building-blocks from density-based clustering. This framework is not only efficient and effective, but it is also statistically sound. Experimental results on a large collection of datasets show the advantages of the proposed framework.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 30th International Conference on Scientific and Statistical Database Management : SSDBM '18 |
| Editors | Michael Bohlen, Johann Gamper, Peer Kroger, Dimitris Sacharidis |
| Number of pages | 12 |
| Publisher | Association for Computing Machinery |
| Publication date | 9. Jul 2018 |
| Article number | 11 |
| ISBN (Electronic) | 978-1-4503-6505-5 |
| DOIs | |
| Publication status | Published - 9. Jul 2018 |
| Event | International Conference on Scientific and Statistical Database Management - Bolzano-Bozen, Italy Duration: 9. Jul 2018 → 11. Jul 2018 Conference number: 30 http://ssdbm2018.inf.unibz.it/ |
Conference
| Conference | International Conference on Scientific and Statistical Database Management |
|---|---|
| Number | 30 |
| Country | Italy |
| City | Bolzano-Bozen |
| Period | 09/07/2018 → 11/07/2018 |
| Internet address |
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Keywords
- Density-based clustering
- Semi-supervised classification
Cite this
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A unified framework of density-based clustering for semi-supervised classification. / Gertrudes, Jadson Castro; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.
Proceedings of the 30th International Conference on Scientific and Statistical Database Management: SSDBM '18. ed. / Michael Bohlen; Johann Gamper; Peer Kroger; Dimitris Sacharidis. Association for Computing Machinery, 2018. 11.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - A unified framework of density-based clustering for semi-supervised classification
AU - Gertrudes, Jadson Castro
AU - Zimek, Arthur
AU - Sander, Jörg
AU - Campello, Ricardo J. G. B.
PY - 2018/7/9
Y1 - 2018/7/9
N2 - Semi-supervised classification is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatically increasing. In this paper, we introduce a unified framework for semi-supervised classification based on building-blocks from density-based clustering. This framework is not only efficient and effective, but it is also statistically sound. Experimental results on a large collection of datasets show the advantages of the proposed framework.
AB - Semi-supervised classification is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatically increasing. In this paper, we introduce a unified framework for semi-supervised classification based on building-blocks from density-based clustering. This framework is not only efficient and effective, but it is also statistically sound. Experimental results on a large collection of datasets show the advantages of the proposed framework.
KW - Density-based clustering
KW - Semi-supervised classification
U2 - 10.1145/3221269.3223037
DO - 10.1145/3221269.3223037
M3 - Article in proceedings
BT - Proceedings of the 30th International Conference on Scientific and Statistical Database Management
A2 - Bohlen, Michael
A2 - Gamper, Johann
A2 - Kroger, Peer
A2 - Sacharidis, Dimitris
PB - Association for Computing Machinery
ER -