Different individuals, at various stages of their lives, place more importance on one life domain than another. This study generates a set of well-being profiles according to different combinations of aggregated elements of well-being: purpose, community, physical, financial and social well-being. To do this, we used different clustering algorithms to identify hidden clusters of individuals surveyed from 2014–2017 by the Gallup U.S Daily Poll. We tried the following clustering approaches: Gaussian finite mixture model (GFMM) clustering, K-means clustering, and hierarchical clustering. We then compared the number and characteristics of clusters given by each approach. The study can be used to gain more understanding of how changes in well-being vary by hidden clusters in their population. In this presentation, we will discuss each clustering method used and comment on the barriers we face.