Multi-view K-means clustering on big data
- Authors
- Xiao CaiFeiping NieHeng Huang
- Journal
- International Joint Conference on Artificial Intelligence
In The Last Decade
doi.org/w5921556 →Countries where authors are citing Multi-view K-means clustering on big data
This map shows the geographic impact of Multi-view K-means clustering on big data. 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 Multi-view K-means clustering on big data with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Multi-view K-means clustering on big data more than expected).
Fields of papers citing Multi-view K-means clustering on big data
This network shows the impact of Multi-view K-means clustering on big data. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Multi-view K-means clustering on big data.
About Multi-view K-means clustering on big data
This paper, published in 2013, received 313 indexed citations . Written by Xiao Cai, Feiping Nie and Heng Huang covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (246 citations), Artificial Intelligence (183 citations) and Media Technology (50 citations). Published in International Joint Conference on Artificial Intelligence.
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/w5921556.