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.
Question
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.
Solution
A primary advantage of using Gaussian mixture models (GMMs) for clustering is that they can model clusters with different shapes and sizes. Unlike many other clustering algorithms that assume clusters are spherical and all have the same size, GMMs are more flexible because they assume that data points are Gaussian distributed. This allows them to accommodate clusters that are elongated or have other non-spherical shapes. Additionally, GMMs can also handle clusters of different sizes.
Similar Questions
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.
A data scientist wants to apply GMM to a dataset with non-spherical clusters. Which of the following statements is true? GMM is not suitable for clusteringGMM requires the data to be linearGMM can only handle spherical clustersGMM can handle non-spherical clusters
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
You are tasked with clustering customer data using a Gaussian mixture model (GMM). Which type of clustering does GMM perform?Hierarchical clusteringLinear clusteringSoft clusteringHard clustering
What role does the covariance matrix play in the Gaussian components of a GMM?It specifies the likelihood of each component.It determines the mean of each component.It controls the width and orientation of each component.It defines the number of components.
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