AnyDBC: An efficient anytime density-based clustering algorithm for very large complex datasets

Research output: Research - peer-reviewArticle in proceedings

DOI

Original languageEnglish
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Number of pages10
PublisherAssociation for Computing Machinery
Publication year13 Aug 2016
Pages1025-1034
ISBN (Electronic)9781450342322
DOIs
StatePublished - 13 Aug 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: 13 Aug 201617 Aug 2016

Conference

Conference22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
LandUnited States
BySan Francisco
Periode13/08/201617/08/2016
SponsorACM SIGKDD, ACM SIGMOD

    Research areas

  • Active learning, Anytime clustering, Density-based clustering

See relations at Aarhus University Citationformats

ID: 110155004