Data Mining and Knowledge Discovery

1.1k papers and 55.4k indexed citations i.

About

The 1.1k papers published in Data Mining and Knowledge Discovery in the last decades have received a total of 55.4k indexed citations. Papers published in Data Mining and Knowledge Discovery usually cover Artificial Intelligence (740 papers), Signal Processing (349 papers) and Information Systems (314 papers) specifically the topics of Data Mining Algorithms and Applications (227 papers), Data Management and Algorithms (180 papers) and Time Series Analysis and Forecasting (179 papers). The most active scholars publishing in Data Mining and Knowledge Discovery are Christopher J. C. Burges, Zhexue Huang, Eamonn Keogh, Jon Kleinberg, Jerome H. Friedman, Jiawei Han, Steven L. Salzberg, Sreerama K. Murthy, Anthony Bagnall and Geoffrey I. Webb.

In The Last Decade

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.

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).

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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2025