The person appears to be a/an White Male, approximately 30 to 39 years old.
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.
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.