mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
Impact in
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- The R Journal
In The Last Decade
doi.org/10.32614/rj-2016-021 →Countries where authors are citing mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
This map shows the geographic impact of mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. 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 mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models more than expected).
Fields of papers citing mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
This network shows the impact of mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models.
About mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
This paper, published in 2016, received 2.0k indexed citations . Written by Luca Scrucca, Michael Fop and Thomas Brendan Murphy covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Molecular Biology (428 citations), Artificial Intelligence (277 citations), Ecology (208 citations), Genetics (185 citations) and Global and Planetary Change (141 citations). Published in The R Journal.
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/10.32614/rj-2016-021.