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

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.

You have applied GMM to a dataset and obtained the following probabilities for three clusters for a particular data point: 0.5, 0.3, and 0.2. To which cluster should this data point be assigned if a hard assignment is required? Cluster 1Cluster 3None, since soft assignment should be usedCluster 2

Which of the following is NOT a limitation of the k-means algorithm?Select one:a.It is not affected by the scale of the variablesb.It may produce suboptimal results if the clusters are not sphericalc.It is sensitive to the initial placement of centroidsd.It requires the user to specify the number of clusters in advance

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

Which of the following statements regarding clustering are true?<br /> A. 1. DBSCAN can identify noise points in addition to clusters. <br />B. 2. DBSCAN requires the user to specify the number of clusters beforehand. <br />C. 3. Hierarchical clustering results depend on distance and linkage criteria. <br />D. 4. K-means clustering guarantees convergence to the global optimum.