Robust clustering for large datasets of weighted and mixed type data. An application to mental well-being in older adults.

F. Scielzo Ortiz, A. Grané, I. Albarrán

The main objective of this paper is to create profiles of older adults to better
understand differing levels of emotional well-being across Europe. Data comes
from the latest wave of the Survey of Health, Ageing and Retirement in Europe (SHARE), carried out in 26 countries and representing over 181 million aged individuals in
Europe. By using the information of around 80 variables of mixed type, we design
composite indicators focused on quality of life in older age, loneliness, social integration and social connectedness. In a second step, indicators are combined with a collection of descriptive variables by computing pairwise robust generalized Gower
distance for weighted data. Finally, Fast k-medoids clustering algorithm is applied
to obtain the profiles. Pyhton packages PyDistances and FastKmedoids are used for
the computations.

Keywords: Emotional well-being, elderly, Fast k-medoids, robust G-Gower, weighted mixed-type data.

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