Constrained K-means Clustering with Background Knowledge
Impact in
Classified as
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w20473171 →Countries where authors are citing Constrained K-means Clustering with Background Knowledge
This map shows the geographic impact of Constrained K-means Clustering with Background Knowledge. 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 Constrained K-means Clustering with Background Knowledge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Constrained K-means Clustering with Background Knowledge more than expected).
Fields of papers citing Constrained K-means Clustering with Background Knowledge
This network shows the impact of Constrained K-means Clustering with Background Knowledge. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Constrained K-means Clustering with Background Knowledge.
About Constrained K-means Clustering with Background Knowledge
This paper, published in 2001, received 1.6k indexed citations . Written by Kiri L. Wagstaff, Claire Cardie, Seth Rogers and Stefan Schrödl covering the research area of Information Systems, Artificial Intelligence and Signal Processing. It is primarily cited by scholars working on Artificial Intelligence (856 citations), Computer Vision and Pattern Recognition (597 citations), Signal Processing (293 citations), Information Systems (201 citations) and Media Technology (116 citations). Published in International Conference on Machine Learning.
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/w20473171.