A comparison of unsupervised methods based on dichotomous data to identify clusters of airways symptoms: latent class analysis and partitioning around medoids methods

Amaral, Rita; Jacinto, Tiago; Pereira, Ana; Almeida, Rute; Fonseca, João

http://hdl.handle.net/10400.22/14168

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Abstract(s)

Latent class analysis (LCA) and partitioning around medoids (PAM) are popular data-driven methods for partitioning objects based on dichotomous data, remaining not clear which is better for large epidemiological datasets. Hence, we compared these methods in the identification of clusters of subjects with airways symptoms, using a large population-based data from the U.S. National Health and Nutrition Examination Surveys (NHANES).

Keywords

Latent class analysis Airways

URI

http://hdl.handle.net/10400.22/14168

Citation

Amaral, R., Jacinto, T., Pereira, A., Almeida, R., & Fonseca, J. (2018). A comparison of unsupervised methods based on dichotomous data to identify clusters of airways symptoms: Latent class analysis and partitioning around medoids methods. European Respiratory Journal, 52(suppl 62). https://doi.org/10.1183/13993003.congress-2018.PA4429