The person appears to be a/an East Asian Male, approximately 30 to 39 years old.
The key distinctions between Gaussian Mixture Models (GMM) and K-Means clustering lie in their underlying methodologies and applications. GMM is a probabilistic model that accommodates various cluster shapes by utilizing multiple Gaussian distributions, allowing for a more flexible representation of data. In contrast, K-Means clustering is a deterministic algorithm that partitions data into K clusters based on the nearest mean, which can lead to limitations in capturing complex cluster structures. Additionally, GMM provides a measure of uncertainty for cluster assignments, whereas K-Means assigns each point to the nearest cluster without such 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.