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ACM Transactions on Knowledge Discovery From Data | 2025 Impact Factor:6.9 | Cite Score:9.8 | SCOPUS Q1

ACM Transactions on Knowledge Discovery From Data Journal

Impact Factor and Journal Rank of ACM Transactions on Knowledge Discovery From Data

  • About: ACM Transactions on Knowledge Discovery from Data (TKDD), published by ACM, is a leading journal dedicated to the knowledge discovery and analysis of diverse forms of data. The journal provides a comprehensive platform for researchers and practitioners to publish innovative research in data mining and big data analytics.
  • Objective:
    The primary objective of ACM TKDD is to advance the field of knowledge discovery from data by publishing high-quality research papers that address scalable and effective algorithms for data mining and big data analysis. The journal aims to promote the development and application of innovative methodologies in mining diverse types of data.
  • Interdisciplinary Focus:
    ACM TKDD welcomes submissions that cover a wide spectrum of topics including mining brain networks, data streams, multi-media data, high-dimensional data, text, web, and semi-structured data, as well as spatial and temporal data. The journal encourages research on data mining techniques for community generation, social network analysis, and graph mining, fostering interdisciplinary collaboration between computer science, statistics, and other related fields.
  • Peer Review and Publication:
    ACM TKDD employs a rigorous peer-review process to ensure the publication of high-quality and impactful research. The journal publishes original research articles, comprehensive reviews, and technical notes that contribute significantly to the knowledge base of data mining and analytics. By maintaining stringent academic standards, ACM TKDD ensures the reliability and validity of all published work.
  • Impact and Innovation:
    The journal evaluates contributions based on their potential to advance the field of knowledge discovery from data through innovative algorithms, methodologies, and applications. Research demonstrating novel approaches to mining complex and large-scale data sets is highly valued. ACM TKDD aims to stimulate further research and promote the adoption of advanced data mining techniques across various domains.
  • Global Reach:
    With an international readership and contributions from researchers worldwide, ACM TKDD facilitates global collaboration and the exchange of knowledge in data mining and analytics. The journal serves as a pivotal resource for academics, researchers, and industry professionals involved in extracting insights from data. By addressing diverse research challenges, ACM TKDD supports a more interconnected and informed community in the field of knowledge discovery from data.

  • Editor-in-Chief:  Jian Pei

  • Scope: Scalable and Effective Algorithms for Data Mining and Big Data Analysis:
    Development of algorithms that can efficiently handle large-scale datasets and ensure effective knowledge extraction.
  • Mining Brain Networks:
    Techniques for analyzing and understanding complex brain networks and their dynamics using data mining approaches.
  • Mining Data Streams:
    Methods for real-time analysis and extraction of patterns from continuous streams of data.
  • Mining Multi-media Data:
    Approaches to extract insights from heterogeneous multimedia data sources, including images, videos, and audio.
  • Mining High-Dimensional Data:
    Techniques for discovering patterns and structures in datasets with a high number of dimensions.
  • Mining Text, Web, and Semi-structured Data:
    Methods for analyzing textual data, web content, and semi-structured data to uncover hidden patterns and relationships.
  • Mining Spatial and Temporal Data:
    Algorithms for discovering patterns in data that vary over time and space, such as geographic data and time-series data.
  • Data Mining for Community Generation:
    Applications of data mining to identify and understand communities and their dynamics within social networks and other domains.
  • Latest Research Topics for PhD in Machine Learning
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  • Latest Research Topics for PhD in Artificial Intelligence

  • Print ISSN:   1556-4681

    Electronic ISSN:   1556-472X

  • Abstracting and Indexing:  Science Citation Index Expanded, Scopus.

  • Imapct Factor 2025:  6.9

  • Subject Area and Category:  Computer Science

  • Publication Frequency:  Quarterly

  • H Index:  83

  • Best Quartile:

    Q1:  Computer Science (miscellaneous)

    Q2:  

    Q3:  

    Q4:  

  • Cite Score:  9.8

  • SNIP:  1.885

  • Journal Rank(SJR):  1.317