A unified framework of density-based clustering for semi-supervised classification

Jadson Castro Gertrudes; Arthur Zimek; Jörg Sander; Ricardo J. G. B. Campello

doi:10.1145/3221269.3223037

A unified framework of density-based clustering for semi-supervised classification

Jadson Castro Gertrudes, Arthur Zimek, Jörg Sander, Ricardo J. G. B. Campello

Department of Mathematics and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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	https://doi.org/10.1145/3221269.3223037
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/Territory	Italy
City	Bolzano-Bozen
Period	09/07/2018 → 11/07/2018
Internet address	http://ssdbm2018.inf.unibz.it/

Keywords

Density-based clustering
Semi-supervised classification

Documents & Links

10.1145/3221269.3223037

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A unified framework of density-based clustering for semi-supervised classification
Zimek, A. (Speaker)
9. Jul 2018 → 11. Jul 2018
Activity: Talks and presentations › Conference presentations

Cite this

Gertrudes, J. C., Zimek, A., Sander, J., & Campello, R. J. G. B. (2018). A unified framework of density-based clustering for semi-supervised classification. In M. Bohlen, J. Gamper, P. Kroger, & D. Sacharidis (Eds.), Proceedings of the 30th International Conference on Scientific and Statistical Database Management: SSDBM '18 Article 11 Association for Computing Machinery. https://doi.org/10.1145/3221269.3223037

Gertrudes, Jadson Castro ; Zimek, Arthur ; Sander, Jörg et al. / A unified framework of density-based clustering for semi-supervised classification. Proceedings of the 30th International Conference on Scientific and Statistical Database Management: SSDBM '18. editor / Michael Bohlen ; Johann Gamper ; Peer Kroger ; Dimitris Sacharidis. Association for Computing Machinery, 2018.

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title = "A unified framework of density-based clustering for semi-supervised classification",

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.",

keywords = "Density-based clustering, Semi-supervised classification",

author = "Gertrudes, \{Jadson Castro\} and Arthur Zimek and J{\"o}rg Sander and Campello, \{Ricardo J. G. B.\}",

year = "2018",

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doi = "10.1145/3221269.3223037",

language = "English",

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booktitle = "Proceedings of the 30th International Conference on Scientific and Statistical Database Management",

publisher = "Association for Computing Machinery",

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note = "International Conference on Scientific and Statistical Database Management, SSDBM ; Conference date: 09-07-2018 Through 11-07-2018",

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Gertrudes, JC, Zimek, A, Sander, J & Campello, RJGB 2018, A unified framework of density-based clustering for semi-supervised classification. in M Bohlen, J Gamper, P Kroger & D Sacharidis (eds), Proceedings of the 30th International Conference on Scientific and Statistical Database Management: SSDBM '18., 11, Association for Computing Machinery, International Conference on Scientific and Statistical Database Management, Bolzano-Bozen, Italy, 09/07/2018. https://doi.org/10.1145/3221269.3223037

A unified framework of density-based clustering for semi-supervised classification. / Gertrudes, Jadson Castro; Zimek, Arthur; Sander, Jörg et al.
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.

N1 - Conference code: 30

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

T2 - International Conference on Scientific and Statistical Database Management

Y2 - 9 July 2018 through 11 July 2018

ER -

Gertrudes JC, Zimek A, Sander J, Campello RJGB. A unified framework of density-based clustering for semi-supervised classification. In Bohlen M, Gamper J, Kroger P, Sacharidis D, editors, Proceedings of the 30th International Conference on Scientific and Statistical Database Management: SSDBM '18. Association for Computing Machinery. 2018. 11 doi: 10.1145/3221269.3223037

A unified framework of density-based clustering for semi-supervised classification

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A unified framework of density-based clustering for semi-supervised classification

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