Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
In The Last Decade
doi.org/w66148631 →Countries where authors are citing Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
This map shows the geographic impact of Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 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 Proceedings of the Second International Conference on Knowledge Discovery and Data Mining with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Proceedings of the Second International Conference on Knowledge Discovery and Data Mining more than expected).
Fields of papers citing Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
This network shows the impact of Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Proceedings of the Second International Conference on Knowledge Discovery and Data Mining.
About Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
This paper, published in 1996, received 800 indexed citations . Written by Evangelos Simoudis, Jiawei Han and Usama M. Fayyad covering the research area of Information Systems. It is primarily cited by scholars working on Artificial Intelligence (283 citations), Computer Vision and Pattern Recognition (124 citations) and Molecular Biology (105 citations).
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.
This paper is also available at doi.org/w66148631.