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IEEE Transactions on Knowledge and Data Engineering is a peer-reviewed journal published by IEEE, covering Data Management and Algorithms, Advanced Database Systems and Queries, Advanced Graph Neural Networks. Over its lifetime it has published 8,048 papers, which have received 461,262 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Knowledge and Data Engineering style. No sign-up. |
| Publishes research in | Data Management and Algorithms Advanced Database Systems and Queries Advanced Graph Neural Networks Complex Network Analysis Techniques Data Mining Algorithms and Applications |
| ISSN | 1041-4347 |
| Citation impact (2-yr) | 8.73 |
| h-index | 267 |
| i10-index | 4,987 |
| Top institutions publishing here | Hong Kong University of Science and Technology |
| Journal website | www.computer.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we…
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and…
With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many…
Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label…
This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals unattempted combinations, and provides guidelines in selecting feature selection algorithms. With the categorizing framework, we continue our…
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