The IEEE ICDM 2020 Workshops

Giuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The 20th IEEE International Conference on Data Mining (ICDM) hosts many co-located workshops, whose papers are traditionally published in a dedicated IEEE CS press proceedings. The purpose of these workshops is to give exposure to the current trends in data mining research, which do not find space or sufficient attention in the main conference tracks either because of the specialised application domain or because of the emerging nature of the field. The aim is to disseminate advances in these emerging fields and to attract more attention and research effort from the community. The quality of the papers and of the final program is guaranteed by an initial selection process of the workshop proposals and by a rigorous peer-review process within each workshop. This volume contains all the papers accepted for publication in the ICDM 2020 workshops and represents an interesting snapshot of data mining methods and applications of emerging and innovative areas of interest. This editorial provides an overview of the workshops included in the final program of ICDM 2020.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
EditorsGiuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherIEEE Computer Society
Pages26-29
Number of pages4
ISBN (Electronic)9781728190129
DOIs
StatePublished - Nov 2020
Event20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 - Virtual, Sorrento, Italy
Duration: Nov 17 2020Nov 20 2020

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2020-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
Country/TerritoryItaly
CityVirtual, Sorrento
Period11/17/2011/20/20

Keywords

  • Data-Mining

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