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 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.