When starting the GMM algorithm, how are the initial Gaussian parameters chosen? By manual selection RandomlyBy using k-means clustering results By sorting the data

While running the Expectation-Maximization algorithm in GMM, what is the purpose of the Expectation step? To terminate the algorithm if convergence is reachedTo update the Gaussian parametersTo calculate the probabilities of data points belonging to each clusterTo assign data points to the nearest cluster

What purpose does the Expectation-Maximisation (EM) algorithm serve in the Gaussian Mixture Model (GMM)?Updating the Gaussian parameters to best fit the data.Calculating the probability density function of the data.Initialising the parameters of the Gaussian components.Assigning data points to clusters based on their likelihood.

What is a primary advantage of using Gaussian mixture models (GMMs) for clustering?They require fewer computational resources compared to other methods.They are simpler to implement than other clustering algorithms.They can model clusters with different shapes and sizes.They always produce spherical clusters.

Which method is commonly used to determine the optimal number of Gaussian components in a GMM?Cross-validationMean Squared Error (MSE) Bayesian Information Criterion (BIC)Silhouette score

You are using GMM to cluster a high-dimensional dataset. How is the covariance matrix represented for each cluster?As a diagonal matrixAs a full matrixAs a vectorAs a scalar