The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.

Supported by the International Federation of Classification Societies, and funded by the Italian, German, and Japanese Classification Societies (CLADAG, GfKl, JCS).

Officially cited as: Adv Data Anal Classif

  • Presents research and applications on the extraction of knowable aspects from many types of data
  • Topics include structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets
  • Shows how new domain-specific knowledge can be made available from data by skillful use of data analysis methods

Journal information

Editors
  • Maurizio Vichi,
  • Wolfgang Gaul,
  • Akinori Okada,
  • Claus Weihs
Publishing model
Hybrid. Open Access options available

Journal metrics

1.603 (2019)
Impact factor
1.888 (2019)
Five year impact factor
74 days
Submission to first decision
407 days
Submission to acceptance
45,020 (2019)
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Latest issue

Volume 14

Issue 2, June 2020

Innovations on Model Based Clustering and Classification

Latest articles

This journal has 34 open access articles

Journal updates

  • COVID-19 and impact on peer review

    As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times.  Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time.

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About this journal

Electronic ISSN
1862-5355
Print ISSN
1862-5347
Abstracted and indexed in
  1. ACM Digital Library
  2. AGRICOLA
  3. CNKI
  4. DBLP
  5. Dimensions
  6. EBSCO Discovery Service
  7. EI Compendex
  8. Google Scholar
  9. INIS Atomindex
  10. INSPEC
  11. Institute of Scientific and Technical Information of China
  12. Japanese Science and Technology Agency (JST)
  13. Journal Citation Reports/Science Edition
  14. Mathematical Reviews
  15. Naver
  16. OCLC WorldCat Discovery Service
  17. ProQuest Advanced Technologies & Aerospace Database
  18. ProQuest Central
  19. ProQuest SciTech Premium Collection
  20. ProQuest Technology Collection
  21. ProQuest-ExLibris Primo
  22. ProQuest-ExLibris Summon
  23. SCImago
  24. SCOPUS
  25. Science Citation Index Expanded (SciSearch)
  26. UGC-CARE List (India)
  27. WTI Frankfurt eG
  28. zbMATH
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