Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Aims & Scope

The objectives of WIREs DMKD are to (a) present the current state of the art of data mining and knowledge discovery through an ongoing series of reviews written by leading researchers, (b) capture the crucial interdisciplinary flavor of the field by including articles that address the key topics from the differing perspectives of data mining and knowledge discovery, including a variety of application areas in technology, business, healthcare, education, government and society and culture, (c) capture the rapid development of data mining and knowledge discovery through a systematic program of content updates, and (d) encourage active participation in this field by presenting its achievements and challenges in an accessible way to a broad audience.

The content of WIREs DMKD will be useful to upper-level undergraduate and postgraduate students, to teaching and research professors in academic programs, and to scientists and research managers in industry.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	11.7
2024	6.40

SJR (SCImago Journal Rank)

Year	Value
2024	2.202

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	79

Journal Rank

Year	Value
2024	1197

Journal Citation Indicator

Year	Value
2024	6381

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Ensemble learning: A survey

  Citation: 1970

  Authors: Omer, Lior

• Classification and regression trees

  Citation: 1658

  Authors: Wei‐Yin

• Algorithms for hierarchical clustering: an overview

  Citation: 1178

  Authors: Fionn, Pedro

• Hyperparameters and tuning strategies for random forest

  Citation: 1160

  Authors: Philipp, Marvin N., Anne‐Laure

• Deep learning for sentiment analysis: A survey

  Citation: 1085

  Authors: Lei, Shuai, Bing

• Causability and explainability of artificial intelligence in medicine

  Citation: 1007

  Authors: Andreas, Georg, Helmut, Kurt, Heimo

• Density‐based clustering

  Citation: 596

  Authors: Hans‐Peter, Peer, Jörg, Arthur

• Bias in data‐driven artificial intelligence systems—An introductory survey

  Citation: 589

  Authors: Eirini, Pavlos, Ujwal, Vasileios, Wolfgang, Maria‐Esther, Salvatore, Franco, Symeon, Emmanouil, Ioannis, Katharina, Claudia, Fariba, Miriam, Harith, Bettina, Tina, Christian, Klaus, Gjergji, Thanassis, Steffen

• Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

  Citation: 568

  Authors: Anne‐Laure, Silke, Jochen, Inke R.

• Educational data mining and learning analytics: An updated survey

  Citation: 555

  Authors: Cristobal, Sebastian

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