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 is a simpler algorithm that partitions data into clusters based on the nearest mean. Additionally, GMM provides a probability distribution for each data point belonging to a cluster, offering a measure of uncertainty, whereas K-Means assigns each point to the nearest cluster without any probabilistic interpretation.
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.