DATA MINING AND KNOWLEDGE DISCOVERY
Scope & Guideline
Illuminating Pathways in the World of Data Mining
Introduction
Aims and Scopes
- Data Mining Techniques:
The journal publishes research on various data mining techniques, including classification, clustering, regression, and anomaly detection, emphasizing novel algorithms and their performance in real-world applications. - Knowledge Discovery in Databases:
Contributions that explore the entire process of knowledge discovery, from data preprocessing and transformation to data analysis and interpretation, are a central theme. - Explainable AI and Interpretability:
A significant focus is placed on developing methods for explainable AI, enhancing the interpretability of complex models, and providing insights into decision-making processes in machine learning. - Time Series Analysis:
Research on methodologies for analyzing time series data, including classification, forecasting, and anomaly detection, is frequently highlighted, reflecting the importance of temporal data in various domains. - Graph and Network Analysis:
The journal covers advancements in graph mining and network analysis, focusing on algorithms for community detection, link prediction, and structural analysis of networks. - Fairness and Ethics in AI:
There is a growing emphasis on the ethical implications of data mining and machine learning, including fairness, bias mitigation, and the social impact of algorithms. - Data Stream Mining:
The journal actively publishes research on algorithms and frameworks for real-time data stream mining, addressing the challenges posed by continuous and high-velocity data. - Multimodal and Heterogeneous Data Integration:
Research that integrates diverse data types (e.g., text, images, and structured data) and develops methods for knowledge extraction from heterogeneous datasets is also a key area.
Trending and Emerging
- Explainable AI and Interpretability:
The increasing complexity of models has led to a surge in research focused on making AI systems more interpretable, with a strong emphasis on developing techniques that allow users to understand model predictions. - Deep Learning Approaches:
Deep learning continues to dominate the landscape, with an increasing number of publications exploring its application in various domains, particularly in time series analysis and image processing. - Fairness and Bias Mitigation:
Research focused on ensuring fairness in machine learning models and mitigating bias has gained traction, reflecting a broader societal concern about the ethical implications of AI. - Automated Machine Learning (AutoML):
The trend towards automating the machine learning pipeline is becoming more prominent, with studies on AutoML frameworks that simplify model selection and hyperparameter tuning. - Graph Neural Networks (GNNs):
The rise of graph neural networks has sparked significant interest, with publications exploring their applications in social networks, recommendation systems, and knowledge graph embeddings. - Real-Time Data Processing and Streaming Analytics:
As data generation accelerates, there is a growing focus on real-time data processing techniques and algorithms for mining data streams, reflecting the need for immediate insights. - Multimodal Learning:
Research integrating multiple data modalities (e.g., text, images, and structured data) is on the rise, driven by the need to leverage diverse information sources for improved knowledge extraction.
Declining or Waning
- Traditional Statistical Methods:
There is a noticeable shift away from purely statistical methods for data analysis towards more complex machine learning techniques, reducing the publication of traditional statistical approaches. - Basic Machine Learning Models:
As the field advances, there is less emphasis on basic machine learning models (e.g., simple regression or basic classifiers) as researchers focus on more sophisticated and hybrid models. - Domain-Specific Applications:
Research that is highly focused on narrow, domain-specific applications is declining, as there is a broader interest in generalizable methodologies that can be applied across various fields. - Manual Feature Engineering:
The trend is moving towards automated feature extraction and representation learning, leading to a decrease in publications centered around manual feature engineering techniques. - Single-Method Studies:
There is a reduction in the publication of studies that evaluate only one method in isolation, with a growing preference for comparative studies and ensemble approaches.
Similar Journals
IEEE INTELLIGENT SYSTEMS
Advancing Knowledge in Intelligent SystemsIEEE Intelligent Systems, published by the renowned IEEE Computer Society, stands at the forefront of research in the fields of Artificial Intelligence and Computer Networks and Communications. With an impressive Q1 ranking in both categories as of 2023 and Scopus rankings placing it in the top 5% of its field, this journal not only showcases cutting-edge scientific advancements but also serves as a vital resource for practitioners, academics, and students seeking to deepen their understanding and application of intelligent systems. The journal covers a broad range of topics including machine learning, data mining, and system architectures, reflecting its commitment to addressing contemporary challenges and innovations in technology. Although it does not offer open access, the journal's research contributions are invaluable, ensuring that its readership remains engaged with the latest findings and applications in a rapidly evolving field. For those interested in submitting high-quality research or staying updated on the latest developments, IEEE Intelligent Systems represents an essential hub of knowledge.
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Advancing interdisciplinary research in data mining and knowledge discovery.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery is a premier journal published by WILEY PERIODICALS, INC, dedicated to the rapidly evolving fields of data mining and knowledge discovery. Since its inception in 2011, this journal has emerged as a vital resource for researchers and practitioners alike, boasting an impressive Q1 ranking in the field of Computer Science (miscellaneous) as of 2023, and is recognized for achieving a remarkable 98th percentile in Scopus rankings. The journal aims to disseminate high-quality, interdisciplinary research that addresses the theoretical and practical implications of data mining techniques, innovative algorithms, and knowledge discovery paradigms. With a commitment to fostering knowledge exchange among academia and industry, Wiley Interdisciplinary Reviews is essential for those looking to remain at the forefront of data science advancements. Researchers and professionals will find relevant discussions, cutting-edge methodologies, and comprehensive reviews that contribute significantly to the field's development.
Social Network Analysis and Mining
Unlocking Insights Through Social ConnectionsSocial Network Analysis and Mining, published by SPRINGER WIEN, is a premier journal dedicated to the advanced study and application of social network analysis and data mining methodologies within the fields of communication, computer science, and media technology. With rigorous peer-review standards and a 2023 category ranking within the Q1 quartile for Communication and various Q2 rankings across Computer Science and Human-Computer Interaction, this journal not only highlights cutting-edge research but also influences current trends and methodologies in the discipline. The journal's articles are accessible through subscription, ensuring a broad reach among academic institutions and professionals. Covering converged years from 2011 to 2024, Social Network Analysis and Mining serves as an essential resource for researchers, practitioners, and students seeking to deepen their understanding of social networks’ roles in information systems and digital communication, reflecting significant insights from its Scopus rankings that position it at a high percentile among related fields.
Big Data Mining and Analytics
Catalyzing Interdisciplinary Collaboration in AnalyticsBig Data Mining and Analytics, published by TSINGHUA UNIVERSITY PRESS, stands at the forefront of interdisciplinary research in the fields of Artificial Intelligence, Computer Networks and Communications, Computer Science Applications, and Information Systems. With an impressive Q1 ranking in multiple categories as of 2023, this journal serves as a critical platform for researchers and professionals eager to explore innovative techniques and methodologies related to big data analytics. Since its transition to Open Access in 2018, Big Data Mining and Analytics has aimed to increase the visibility and accessibility of its cutting-edge research, making permanent strides in the global academic landscape. Housed in Beijing, China, and actively embracing the converged years from 2018 to 2024, the journal aims to cultivate a rich discourse on emerging trends and applications, ensuring its relevance in a rapidly evolving technological environment. Join a vibrant community of scholars dedicated to advancing the frontiers of knowledge in big data.
ACM Transactions on Knowledge Discovery from Data
Catalyzing Progress in Knowledge Discovery from DataACM Transactions on Knowledge Discovery from Data (TKDD), published by the Association for Computing Machinery, is a prestigious journal at the forefront of the interdisciplinary realm of data mining and knowledge discovery. With an impressive Q1 ranking in Computer Science and a Scopus rank of #43 out of 232, this journal stands out as a top-tier resource for innovative research that addresses complex challenges in data science. Covering impactful studies from 2007 to 2024, TKDD presents cutting-edge algorithms, methodologies, and applications that shape the future of knowledge extraction from vast datasets. While not an open-access journal, it provides a platform for researchers, professionals, and students to disseminate their findings and engage with the latest advancements in this rapidly evolving field. By fostering collaboration and knowledge sharing, TKDD plays a vital role in advancing the understanding and application of data analysis techniques, making it an essential read for anyone involved in the pursuit of knowledge from data.
International Journal of Data Warehousing and Mining
Elevating Standards in Data Research ExcellenceInternational Journal of Data Warehousing and Mining, published by IGI Global, is a vital resource in the field of data management and analytics, catering to researchers, professionals, and students alike. With ISSN 1548-3924 and E-ISSN 1548-3932, this journal has been at the forefront of disseminating pioneering research since its inception in 2005 and will continue to do so through 2024. Despite its current categorization in the Q4 quartile for Hardware and Architecture as well as Software, and its Scopus rankings, the journal aims to foster innovation within the domains of data warehousing, data mining, and their applications across various sectors. The absence of an open access option does not diminish its significance; rather, it ensures that the journal maintains rigorous peer-review standards, providing high-quality research outputs that contribute to ongoing discussions and advancements within the field. Researchers and practitioners looking to stay updated on the latest trends and methodologies will find the International Journal of Data Warehousing and Mining an indispensable tool in their academic and professional endeavors.
Acta Universitatis Sapientiae Informatica
Empowering Research Through Open AccessActa 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 Science and Engineering
Elevating knowledge at the crossroads of technology and data.Data Science and Engineering is a premier open access journal published by SPRINGERNATURE, dedicated to advancing the fields of data science, artificial intelligence, computational mechanics, and information systems. Since its inception in 2016, this journal has rapidly established itself as a leader in the academic community, boasting an impressive Q1 ranking in multiple computer science categories, including Artificial Intelligence, Software, and Information Systems. With a commitment to disseminating high-quality research, it caters to a diverse audience of researchers, professionals, and students eager to explore the intersection of data and technology. The journal's robust global reach, combined with its respected reputation, empowers authors to share their findings widely, facilitating breakthroughs and innovations across the digital landscape. Join the vibrant community of scholars contributing to this integral field of study, and stay informed with the latest research by accessing the journal freely online.
International Journal of Intelligent Engineering Informatics
Exploring Innovations in AI and Human-Computer Interaction.International Journal of Intelligent Engineering Informatics, published by INDERSCIENCE ENTERPRISES LTD, stands at the forefront of research in the interdisciplinary domains of computer science, artificial intelligence, and human-computer interaction. With an ISSN of 1758-8715 and E-ISSN of 1758-8723, this journal serves as a vital resource for researchers and professionals seeking to explore the latest advancements in intelligent engineering and informatics techniques crucial for the evolution of modern technologies. Although currently not an open-access publication, it provides a necessary platform for disseminating high-quality research; its impact factor continues to grow, attracting a diverse readership interested in signal processing, software development, and computer vision. Covering innovative topics from 2022 to 2024, the journal is committed to fostering scholarly dialogue that paves the way for emerging trends and applications in the field, ensuring its relevance and significance in today's rapidly advancing technological landscape.
SOFT COMPUTING
Navigating the Complexities of Modern Computational ChallengesSOFT 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.