ACM Transactions on Knowledge Discovery from Data
Scope & Guideline
Shaping Tomorrow’s Knowledge through Data Insights
Introduction
Aims and Scopes
- Knowledge Discovery Techniques:
The journal prioritizes research on a wide range of knowledge discovery methods, including but not limited to data mining, machine learning, and statistical analysis, aimed at extracting actionable insights from large datasets. - Graph and Network Analysis:
A significant focus is placed on methodologies for analyzing and processing data represented as graphs or networks, exploring topics such as community detection, link prediction, and network dynamics. - Fairness and Ethical AI:
Recent publications show a growing emphasis on fairness in algorithms, addressing bias and ethical considerations in automated decision-making processes, which is crucial in the context of AI and machine learning. - Federated Learning and Privacy-Preserving Techniques:
The journal covers advancements in federated learning and privacy-preserving methods, highlighting the importance of data security and privacy in knowledge discovery. - Interdisciplinary Applications:
TKDD publishes work that spans various disciplines, showcasing applications of knowledge discovery in fields such as healthcare, social media, finance, and urban planning, demonstrating the versatility of data mining techniques.
Trending and Emerging
- Explainable AI and Interpretability:
There is a significant rise in research focusing on explainability and interpretability of machine learning models, driven by the need for transparency in AI systems, especially in sensitive applications such as healthcare and finance. - Dynamic and Temporal Data Analysis:
An increasing number of publications address challenges associated with dynamic and temporal data, reflecting the growing importance of time-series analysis and the need to model changes over time in various applications. - Causality and Causal Inference:
Emerging interest in causal inference methods signals a trend towards understanding relationships and effects rather than mere correlations, enhancing the applicability of knowledge discovery in real-world scenarios. - Data Privacy and Security:
With the rise of data privacy concerns, there is a growing emphasis on privacy-preserving data mining techniques and federated learning approaches that allow for collaborative analysis without compromising sensitive information. - Integration of Multi-modal Data Sources:
Research integrating various data types (text, images, time-series) is becoming more prevalent, reflecting the need for versatile approaches that can handle the complexity of real-world data.
Declining or Waning
- Traditional Statistical Methods:
There has been a decline in papers focusing solely on classical statistical methods for data analysis, as the field moves toward more complex machine learning and deep learning approaches. - Basic Clustering Techniques:
The frequency of papers presenting basic clustering algorithms has diminished, likely due to the emergence of more sophisticated and hybrid methods that better handle the complexities of modern datasets. - Simple Data Visualization Techniques:
Research dedicated to basic data visualization techniques has waned, as the community increasingly seeks advanced methods that incorporate interactivity and dynamic data representation.
Similar Journals
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
Fostering Multidisciplinary Insights in AI and NetworksThe Journal of Intelligent Information Systems, published by Springer since 1992, is a premier academic journal that offers a multidisciplinary platform in the fields of Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, Information Systems, and Software. With an impressive impact reflected in its 2023 Q2 category rankings across multiple domains and a commendable standing in the Scopus Rankings—ranking #84 in Computer Networks and Communications and #101 in Artificial Intelligence—the journal is recognized for its contribution to advancing knowledge and innovation. Although it is not an open-access journal, its accessibility through institutional subscriptions ensures that a wide range of researchers, professionals, and students can engage with high-quality, peer-reviewed research that addresses the latest advancements and trends in intelligent systems. For over three decades, this journal has effectively bridged gaps between academia and industry, making it a vital resource for those aiming to push boundaries in intelligent information systems.
Advances in Data Science and Adaptive Analysis
Discovering New Frontiers in Data Science Research.Advances in Data Science and Adaptive Analysis is a prestigious journal dedicated to the advancement of knowledge within the rapidly evolving fields of data science and adaptive analysis. Published by WORLD SCIENTIFIC PUBL CO PTE LTD, this journal aims to serve as a platform for researchers, professionals, and students to disseminate innovative findings and methodologies. With a focus on interdisciplinary approaches, it invites contributions that explore the application of adaptive techniques in tackling complex data-driven challenges. Situated in Singapore, the journal stands out for its commitment to high-quality research, making significant impacts in the academic community and beyond. Although the journal currently does not offer open access, it remains a crucial resource for those striving to push the boundaries of data science research and its practical applications.
Acta Universitatis Sapientiae Informatica
Exploring New Horizons in Computer ScienceActa Universitatis Sapientiae Informatica, published by SCIENDO, is an esteemed open-access journal in the field of computer science and informatics. Since its transition to open access in 2013, the journal has fostered an inclusive academic environment that allows researchers, professionals, and students to freely access cutting-edge research and innovations. With its ISSN 1844-6086 and E-ISSN 2066-7760, Acta Universitatis Sapientiae Informatica aims to disseminate high-quality scholarly articles that cover a broad scope of topics ranging from theoretical foundations to practical applications in informatics. Located in Warsaw, Poland, the journal serves as an essential platform for advancing the discourse in computer science, thus playing a critical role in both regional and international research communities.
DATA MINING AND KNOWLEDGE DISCOVERY
Pioneering Research in Data Mining and Knowledge DiscoveryDATA MINING AND KNOWLEDGE DISCOVERY, published by Springer, stands as a premier journal within the realms of Computer Networks and Communications, Computer Science Applications, and Information Systems. With an impressive impact factor and a notable presence in various rankings—achieving the Q1 category in 2023—we invite researchers, professionals, and students alike to explore cutting-edge methodologies and innovative applications in data mining. Established in 1997 and continuing its journey through to 2024, the journal not only contributes significantly to the advancement of knowledge in computational techniques but also fosters an understanding of the complex interrelations in data systems. Though not an open access publication, it offers a wealth of insights crucial for driving advancements in technology and analytics. Based in Dordrecht, Netherlands, the journal remains dedicated to disseminating high-quality research and is essential reading for anyone engaged in the ever-evolving field of data science.
KNOWLEDGE AND INFORMATION SYSTEMS
Bridging Theory and Practice in Information SystemsKNOWLEDGE AND INFORMATION SYSTEMS, published by SPRINGER LONDON LTD, is a distinguished journal in the field of information systems, artificial intelligence, and human-computer interaction. With its ISSN 0219-1377 and E-ISSN 0219-3116, this journal has built a robust reputation since its inception, featuring a convergence of valuable research from 2005 through 2024. Catering to a diverse academic audience, it is classified among the leading journals in its category, proudly holding a Q1 ranking in Information Systems and Q2 rankings in multiple other domains. The journal aims to publish cutting-edge research that not only advances theoretical understanding but also provides practical applications within these rapidly evolving fields. Although it is not an Open Access journal, subscribers can access a wealth of knowledge critical for researchers, practitioners, and students looking to enhance their expertise. With a 2023 Scopus rank placing it within the 66th percentile for Information Systems, KNOWLEDGE AND INFORMATION SYSTEMS is an invaluable resource for those committed to pushing the frontiers of knowledge in technology and information science.
SOFT COMPUTING
Unleashing the Power of Genetic Algorithms and BeyondSOFT COMPUTING is a premier international journal published by Springer, focusing on the interdisciplinary field of soft computing, which includes areas such as fuzzy logic, neural networks, genetic algorithms, and their applications. With an ISSN of 1432-7643 and E-ISSN 1433-7479, the journal is based in Germany and contributes significantly to the advancement of knowledge in its fields, boasting an impressive Scopus ranking that places it in the top echelons of Geometry and Topology, Theoretical Computer Science, and Software categories. In the 2023 category quartiles, it has achieved Q2 rankings in multiple disciplines, reflecting its high-quality research contributions. Though not Open Access, the journal's rigor and relevance to contemporary issues make it a favored resource for researchers, professionals, and students alike. From its inception in 2000 and spanning across the years until 2024, SOFT COMPUTING continues to serve as a robust platform for innovative research and theoretical advancements, making it an essential read for anyone engaged in the rapidly evolving landscape of computational intelligence.
Statistical Analysis and Data Mining
Pioneering New Frontiers in Statistical AnalysisStatistical Analysis and Data Mining is a leading journal published by WILEY, dedicated to exploring the latest advancements in statistical methods and data mining techniques. With an ISSN of 1932-1864 and an E-ISSN of 1932-1872, this journal serves as a significant platform for researchers and professionals in statistical analysis, computer science applications, and information systems. Covering a wide range of topics from innovative analytical methodologies to emerging data mining algorithms, the journal aims to disseminate high-quality research that contributes to the evolving landscape of data science. Ranked in the Q2 category for the fields of Analysis, Computer Science Applications, and Information Systems in 2023, it emphasizes its relevance and impact within academia. While it offers limited Open Access options, the insights shared in this publication are integral for those wishing to stay ahead in fast-paced research and data-driven industries. Since its inception in 2008 and continuing through 2024, Statistical Analysis and Data Mining invites submissions that reflect rigorous empirical research coupled with practical implications, making it a vital resource for the academic community.
Machine Learning and Knowledge Extraction
Empowering Researchers with Open Access to Cutting-Edge FindingsMachine Learning and Knowledge Extraction, published by MDPI, is an esteemed Open Access journal that has been at the forefront of disseminating cutting-edge research since its inception in 2019. Based in Switzerland, this journal has established itself as a significant contributor to the fields of Artificial Intelligence and Engineering, currently ranking in the Q2 category in Artificial Intelligence and Q1 in Engineering (miscellaneous) for 2023. With a notable Scopus ranking, it holds the 35th position out of 204 in Engineering, placing it in the 83rd percentile, while it ranks 127th out of 350 in Computer Science, reaching the 63rd percentile. Machine Learning and Knowledge Extraction serves as a vital platform for researchers, professionals, and students alike, promoting insightful discussions, innovative methodologies, and profound discoveries in machine learning and data extraction techniques. The journal's open access model ensures that groundbreaking research is widely accessible, fostering collaboration and advancing knowledge across various disciplines.
Foundations of Data Science
Connecting Theory and Practice in Data ScienceFoundations of Data Science, published by the American Institute of Mathematical Sciences (AIMS), is a pioneering journal dedicated to advancing knowledge within the ever-evolving fields of data science, mathematics, and computational theory. With an impact factor reflecting its quality and relevance, this journal has established itself as a crucial resource for researchers and professionals alike, achieving remarkable rankings in the Scopus metrics across various mathematical categories, including 35th in Analysis and 70th in Statistics and Probability. The journal, which has been continuously growing in significance since its inception in 2019, focuses on both foundational theories and applied methodologies, providing open access to cutting-edge research from 2024 onward. Its commitment to fostering interdisciplinary collaboration ensures that it remains at the forefront of the data science realm, making it an essential platform for students, scholars, and practitioners aiming to deepen their understanding and contribute to the scientific community.
Frontiers of Computer Science
Fostering Dialogue that Shapes the Future of TechnologyFrontiers of Computer Science is a leading peer-reviewed journal dedicated to advancing the field of computer science through the publication of high-quality research articles, reviews, and theoretical discussions. Published by HIGHER EDUCATION PRESS, this journal has gained significant recognition, currently boasting a prestigious impact factor and ranking in the Q1 quartile for both Computer Science (miscellaneous) and Theoretical Computer Science categories in 2023. With a focus on the intersection of computational theory and practical applications, it serves as a vital platform for researchers, professionals, and students alike who are eager to contribute to and stay updated with groundbreaking developments. The journal’s scope encompasses a wide range of topics, reflecting the diverse nature of computer science today. Operating from Beijing, China, it emphasizes Open Access, ensuring that vital research is readily available to the global academic community. With its convergence period spanning from 2013 to 2024, Frontiers of Computer Science remains committed to fostering innovation and scholarly dialogue that drives the future of technology.