Högskolan i Skövde

his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Maximal c consensus meets
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab)ORCID iD: 0000-0002-0368-8037
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab)ORCID iD: 0000-0002-2564-0683
2019 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 51, p. 58-66Article in journal (Refereed) Published
Abstract [en]

Given a set S of subsets of a reference set X, we define the problem of finding c subsets of X that maximize the size of the intersection among the included subsets. Maximizing the size of the intersection means that they are subsets of the sets in S and they are as large as possible. We can understand the result of this problem as c consensus sets of S, or c consensus representatives of S. From the perspective of lattice theory, each representative will be a meet of some sets in S. In this paper we define formally this problem, and present heuristic algorithms to solve it. We also discuss the relationship with other established problems in the literature.

Place, publisher, year, edition, pages
NETHERLANDS: Elsevier, 2019. Vol. 51, p. 58-66
Keywords [en]
clustering, consensus clustering, heuristic algorithms, Maximal c consensus meets, Cluster analysis, Clustering algorithms, Lattice theory, Set theory, Reference set
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16463DOI: 10.1016/j.inffus.2018.09.011ISI: 000469155600006Scopus ID: 2-s2.0-85056612105OAI: oai:DiVA.org:his-16463DiVA, id: diva2:1283853
Part of project
Disclosure risk and transparency in big data privacy, Swedish Research Council
Funder
Swedish Research Council, 2016–03346
Note

Partially supported by Vetenskapsrådet project: “Disclosure risk and transparency in big data privacy” (VR 2016–03346).

DRIAT

Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2021-08-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Torra, VicençSenavirathne, Navoda

Search in DiVA

By author/editor
Torra, VicençSenavirathne, Navoda
By organisation
School of InformaticsThe Informatics Research Centre
In the same journal
Information Fusion
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 284 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf