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

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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-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 languageEnglish
Title of host publicationProceedings of the 30th International Conference on Scientific and Statistical Database Management : SSDBM '18
EditorsMichael Bohlen, Johann Gamper, Peer Kroger, Dimitris Sacharidis
Number of pages12
PublisherAssociation for Computing Machinery
Publication date9. Jul 2018
Article number11
ISBN (Electronic)978-1-4503-6505-5
DOIs
Publication statusPublished - 9. Jul 2018
EventInternational Conference on Scientific and Statistical Database Management - Bolzano-Bozen, Italy
Duration: 9. Jul 201811. Jul 2018
Conference number: 30
http://ssdbm2018.inf.unibz.it/

Conference

ConferenceInternational Conference on Scientific and Statistical Database Management
Number30
CountryItaly
CityBolzano-Bozen
Period09/07/201811/07/2018
Internet address

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Keywords

  • Density-based clustering
  • Semi-supervised classification

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 [11] Association for Computing Machinery. https://doi.org/10.1145/3221269.3223037
Gertrudes, Jadson Castro ; Zimek, Arthur ; Sander, Jörg ; Campello, Ricardo J. G. B. / 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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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; 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 proceedingArticle in proceedingsResearchpeer-review

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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 https://doi.org/10.1145/3221269.3223037