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 clustering relies on a simpler approach 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, whereas K-Means does not account for this uncertainty.
The key differences between Gaussian Mixture Models (GMM) and K-Means clustering lie in their underlying methodologies and applications. GMM is a probabilistic model that can capture complex cluster shapes by using multiple Gaussian distributions, making it suitable for scenarios where the data may not be spherical. It also provides a measure of uncertainty for each data point's cluster assignment. In contrast, K-Means clustering is a simpler, deterministic algorithm that partitions data into K clusters based on the nearest mean, which assumes spherical clusters and can struggle with varying cluster shapes. GMM is often preferred in more sophisticated data analysis tasks, especially when dealing with real-world data that may exhibit overlapping clusters.