k-Means clustering MCQs January 8, 2026November 18, 2024 by u930973931_answers 1 min Score: 0 Attempted: 0/1 Subscribe 1. 1. In K-means clustering, what does the “K” represent?2. Which of the following is the objective of the K-means algorithm?3. What is the first step in the K-means clustering algorithm?4. What happens if the value of “K” is set too high in K-means clustering?5. Which of the following is a limitation of the K-means algorithm?6. Which of the following methods can be used to select the optimal number of clusters (K) for K-means clustering?7. Which distance metric is typically used in K-means clustering to calculate the distance between points and centroids?8. What is the role of centroids in K-means clustering?9. In which of the following situations is K-means clustering likely to perform poorly?10. In K-means clustering, what does the “assignment step” involve?11. What happens after the assignment step in K-means clustering?12. Which of the following is a valid way to handle categorical data in K-means clustering?13. Which of the following is a major advantage of K-means clustering?14. In K-means clustering, what happens during the “update step”?15. What is the “Elbow Method” used for in K-means clustering? (A) The number of features (B) The number of clusters (C) The number of iterations (D) The number of nearest neighbors (A) Minimize the within-cluster variance (B) Maximize the distance between clusters (C) Minimize the number of clusters (D) Maximize the number of features in each cluster (A) Assign data points to the nearest cluster centroid (B) Calculate the distance between all points and centroids (C) Randomly initialize K centroids (D) Calculate the mean of all data points (A) The model overfits, creating very small clusters. (B) The model underfits, creating fewer clusters. (C) The algorithm will not converge. (D) The clusters will become more distinct. (A) It works only with numerical data. (B) It is sensitive to the initial placement of the centroids. (C) It does not scale well with large datasets. (D) It requires the number of clusters to be automatically determined. (A) Elbow method (B) Silhouette score (C) Gap statistic (D) All of the above (A) Manhattan distance (B) Euclidean distance (C) Minkowski distance (D) Cosine similarity (A) They represent the center of a cluster. (B) They are data points that are assigned to clusters. (C) They measure the distance between clusters. (D) They are the final outputs of the clustering process. (A) When the clusters are globular and well-separated (B) When the data has outliers or noise (C) When the data has only a few features (D) When the number of clusters is very large (A) Recalculating the centroids (B) Assigning each data point to the nearest centroid (C) Determining the optimal number of clusters (D) Initializing the centroids randomly (A) The algorithm stops and outputs the final clusters. (B) The centroids are updated by calculating the mean of the assigned points. (C) The data points are rearranged in a different order. (D) The algorithm checks for convergence and stops if clusters are not changing. (A) Convert the data into numerical format using encoding techniques like one-hot encoding. (B) Use K-means directly on categorical data without any transformation. (C) Apply distance metrics like Euclidean to categorical data. (D) K-means cannot be applied to categorical data. (A) It can handle large datasets efficiently. (B) It automatically handles outliers. (C) It works well with both numerical and categorical data. (D) It guarantees a global optimal solution. (A) The centroids are randomly initialized. (B) Each point is reassigned to a new cluster. (C) The centroids are recalculated based on the mean of assigned points. (D) The number of clusters is increased or decreased. (A) To calculate the distance between clusters (B) To determine the optimal number of clusters (K) (C) To optimize the placement of centroids (D) To identify the outliers in the dataset