Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health

Often data can be represented as a matrix, e.g., observations as rows and variables as columns, or as a doubly classified contingency table. Researchers may be interested in clustering the observations, the variables, or both. If the data is non-negative, then Non-negative Matrix Factorization (NMF)...

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Detalles Bibliográficos
Autores principales: Fogel, Paul, Gaston-Mathé, Yann, Hawkins, Douglas, Fogel, Fajwel, Luta, George, Young, S. Stanley
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4881134/
https://www.ncbi.nlm.nih.gov/pubmed/27213413
http://dx.doi.org/10.3390/ijerph13050509

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4881134/
https://www.ncbi.nlm.nih.gov/pubmed/27213413
http://dx.doi.org/10.3390/ijerph13050509

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