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Journal Photo for ACM SIGKDD Explorations Newsletter
Peer reviewed only Open Access

ACM SIGKDD Explorations Newsletter (SEN)

Publisher : Association for Computing Machinery
Condensed Matter Physics Mechanics of Materials General Materials Science
e-ISSN 1931-0153
p-ISSN 1931-0145
Issue Frequency Bi-Annual
Est. Year 1999
Mobile 18003426626
Language English
APC YES
Email explorations@kdd.org

Journal Descriptions

ACM SIGKDD Explorations is the official biannual newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), first published in 1999 to provide a dedicated venue for sharing knowledge and developments in data mining and knowledge discovery. Since its inception, it has been published twice yearly and distributed to SIGKDD members and libraries worldwide. The newsletter blends editorial commentary, in-depth technical articles, surveys, research highlights, and community updates, making it a valuable resource for both academic and applied audiences in the fields of data mining, AI, and analytics. SIGKDD Explorations has historically featured contributions from leading researchers and practitioners, offering perspectives on advances in algorithms, methodologies in data analysis, and practical challenges in large-scale data handling. Over time, the newsletter has documented the evolution of data mining and KDD from early methodologies to modern analytics and machine learning. Content typically includes feature articles, interviews, conference reports, and community announcements, fostering engagement within the SIGKDD community and beyond. The publication complements SIGKDD’s flagship annual conference (KDD) by disseminating knowledge between conference cycles and facilitating ongoing dialogue in the rapidly evolving field of data science.

ACM SIGKDD Explorations Newsletter (SEN) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Condensed Matter Physics, Mechanics of Materials, General Materials Science, General Chemistry, Mechanical Engineering, data mining, knowledge discovery, big data, machine learning, and analytics, offering a mix of research surveys, technical articles, community updates, conference reports, editorial insights, bridging academic research with real-world applications and developments in data-driven disciplines , Online or Print , Bi-Annual Journal

  • UGC Approved, ISSN Approved: P-ISSN P-ISSN: 1931-0145, E-ISSN: 1931-0153, Established: 1999,
  • Does Not Provide Crossref DOI
  • Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE

Indexing