Data Mining and Knowledge Discovery

70.5k citations
1.1k papers · · active since 1950

Impact in

    • Time Series Analysis and Forecasting
    • Data Management and Algorithms
    • Anomaly Detection Techniques and Applications
    • Imbalanced Data Classification Techniques

Papers in

Data Mining and Knowledge Discovery

1.0k papers receiving 65.2k citations

Peers

Data Mining and Knowledge Discovery
Comparison fields: 5 of 232
  • Signal Processing 17.7k
  • Artificial Intelligence 35.2k
  • Information Systems 17.0k
  • Computer Vision and Pattern Recognition 11.4k
  • Computational Mathematics 324
Replace ACM Transactions on Information Systems with:
ACM Transactions on Information Systems China
ACM Transactions on Intelligent Systems and Technology China
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) United States
The VLDB Journal United States
Journal of Artificial Intelligence Research United States
ACM Transactions on Knowledge Discovery from Data China
IEEE Intelligent Systems United States
Journal of Statistical Software United States
IEEE Signal Processing Magazine United States
Journal of Machine Learning Research United States
Data Mining and Knowledge Discovery relative to ACM Transactions on Information Systems China ACM Transactions on Information Systems's profile →
Citations per field
00.5×1.5×2.3×
ACM Transactions on Information Systems · 1×
Citations per year

Countries where authors publish in Data Mining and Knowledge Discovery

Since Specialization
Citations

This map shows the geographic impact of research published in 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 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 Data Mining and Knowledge Discovery more than expected).

Fields of papers published in Data Mining and Knowledge Discovery

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in 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 Data Mining and Knowledge Discovery.

About Data Mining and Knowledge Discovery

The 1.1k papers published in Data Mining and Knowledge Discovery in the last decades have received a total of 70.5k indexed citations . Papers published in Data Mining and Knowledge Discovery usually cover Signal Processing (345 papers), Computational Mathematics (17 papers), Artificial Intelligence (740 papers), Information Systems (311 papers) and Statistical and Nonlinear Physics (143 papers) specifically the topics of Data Mining Algorithms and Applications (220 papers), Time Series Analysis and Forecasting (179 papers), Data Management and Algorithms (173 papers), Anomaly Detection Techniques and Applications (152 papers), Complex Network Analysis Techniques (140 papers), Rough Sets and Fuzzy Logic (102 papers), Machine Learning and Data Classification (96 papers) and Advanced Graph Neural Networks (93 papers). The most active scholars publishing in Data Mining and Knowledge Discovery are Christopher J. C. Burges, Zhexue Huang, Eamonn Keogh, Jon Kleinberg, Jiawei Han, Jerome H. Friedman, Sreerama K. Murthy, Hannu Toivonen, Steven L. Salzberg and Anthony Bagnall.

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.

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