• Corpus ID: 249954039

Quantifying Distances Between Clusters with Elliptical or Non-Elliptical Shapes

@inproceedings{Wallace2022QuantifyingDB,
  title={Quantifying Distances Between Clusters with Elliptical or Non-Elliptical Shapes},
  author={Meredith L. Wallace and Lisa M. McTeague and Jessica L. Graves and Nicholas Kissel and Cristina Tortora and Bradley Wheeler and Satish Iyengar},
  year={2022}
}
Finite mixture models that allow for a broad range of potentially non-elliptical clus- ter distributions is an emerging methodological field. Such methods allow for the shape of the clusters to match the natural heterogeneity of the data, rather than forcing a series of elliptical clusters. These methods are highly relevant for clustering continuous non-normal data – a common occurrence with objective data that are now routinely captured in health research. However, interpreting and comparing… 

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