The person appears to be a/an East Asian Male, approximately 20 to 29 years old.
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.
The key distinctions between Gaussian Mixture Models (GMM) and K-Means clustering lie in their methodologies and applications. GMM is a probabilistic model that can accommodate complex cluster shapes by utilizing multiple Gaussian distributions, making it suitable for scenarios where data may not be spherical. In contrast, K-Means clustering is a deterministic algorithm that partitions data into K clusters based on the nearest mean, which assumes spherical clusters. Additionally, GMM provides a measure of uncertainty for cluster assignments, while K-Means offers a more straightforward, but less flexible, approach to clustering.