On Spectral Clustering: Analysis and an algorithm

5.1k indexed citations

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This paper, published in 2001, received 5.1k indexed citations. Written by Andrew Y. Ng, Michael I. Jordan and Yair Weiss covering the research area of Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (2.5k citations), Artificial Intelligence (2.5k citations) and Statistical and Nonlinear Physics (828 citations). Published in neural information processing systems.

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Countries where authors are citing On Spectral Clustering: Analysis and an algorithm

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This map shows the geographic impact of On Spectral Clustering: Analysis and an algorithm. 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 On Spectral Clustering: Analysis and an algorithm with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites On Spectral Clustering: Analysis and an algorithm more than expected).

Fields of papers citing On Spectral Clustering: Analysis and an algorithm

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of On Spectral Clustering: Analysis and an algorithm. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the On Spectral Clustering: Analysis and an algorithm.

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/w5033571.

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