Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
471 papers
receiving
24.3k citations
Peers
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
Comparison fields: 5 of 239
Health Informatics664
Computational Mathematics211
Artificial Intelligence10.4k
Computer Science Applications1.5k
Information Systems4.2k
Replace Computer Science Review with:
Computer Science ReviewIndia
ACM Transactions on Interactive Intelligent SystemsUnited States
User Modeling and User-Adapted InteractionUnited States
ACM Transactions on Internet TechnologyUnited States
Wiley Interdisciplinary Reviews Computational StatisticsUnited States
Transactions of the Association for Computational LinguisticsUnited States
IEEE Computational Intelligence MagazineChina
The Knowledge Engineering ReviewUnited Kingdom
Natural Language EngineeringUnited States
AI CommunicationsSpain
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discoveryrelative toComputer Science ReviewIndiaComputer Science Review's profile →
Citations per field
00.5×2×4×6×8×9.2×
Computer Science Review · 1×
×3.8664/173HI
×9.2211/23CM
×1.210k/9kAI
×3.41k/431CSA
×0.84k/5kIS
Citations per year
'16
'17
'18
'19
'20
'21
'22
'23
'24
'25
'26
Countries where authors publish in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
Since Specialization
Citations
This map shows the geographic impact of research published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery more than expected).
Fields of papers published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
This network shows the impact of papers published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery.
About Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
The 513 papers published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery in the last decades have received a total of 25.7k indexed citations . Papers published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery usually cover Computational Mathematics (6 papers), Artificial Intelligence (299 papers), Signal Processing (83 papers), Health Informatics (9 papers) and Information Systems (145 papers) specifically the topics of Data Mining Algorithms and Applications (78 papers), Data Management and Algorithms (50 papers), Machine Learning and Data Classification (43 papers), Data Stream Mining Techniques (35 papers), Advanced Clustering Algorithms Research (35 papers), Rough Sets and Fuzzy Logic (33 papers), Complex Network Analysis Techniques (32 papers) and Imbalanced Data Classification Techniques (24 papers). The most active scholars publishing in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery are Wei‐Yin Loh, Lior Rokach, Sebastián Ventura, Fionn Murtagh, Pedro Contreras, Cristóbal Romero, Lei Zhang, Shuai Wang, Bing Liu and Inke R. König.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.