The person appears to be a/an White Male, approximately 20 to 29 years old.
The key differences between Gaussian Mixture Models (GMM) and K-Means clustering are that GMM is a probabilistic model that can capture complex cluster shapes through a combination of Gaussian distributions, while K-Means clustering relies on a simpler approach that assigns data points to the nearest centroid, resulting in spherical clusters. Additionally, GMM provides a measure of uncertainty for each data point's cluster membership, whereas K-Means does not account for this uncertainty.
The key differences between Gaussian Mixture Models (GMM) and K-Means clustering are as follows: GMM is a probabilistic model that can capture complex cluster shapes by using multiple Gaussian distributions, allowing for more flexibility in modeling data. In contrast, K-Means clustering is a simpler, iterative algorithm that assigns data points to the nearest centroid, resulting in spherical clusters. Additionally, GMM provides a measure of uncertainty for each data point's cluster membership, while K-Means does not offer this probabilistic interpretation.