Clustering - Ai Quiz Questions

1. The goal of clustering is to-

A. Divide the data points into groups

B. Classify the data point into different classes

C. Predict the output values of input data points

D. All of the above

2. Clustering is a-

A. Supervised learning

B. Unsupervised learning

C. Reinforcement learning

D. None

3. Which of the following clustering algorithms suffers from the problem of convergence at local optima?

A. K- Means clustering

B. Hierarchical clustering

C. Diverse clustering

D. All of the above

4. Which version of the clustering algorithm is most sensitive to outliers?

A. K-means clustering algorithm

B. K-modes clustering algorithm

C. K-medians clustering algorithm

D. None

5. Which of the following is a bad characteristic of a dataset for clustering analysis-

A. Data points with outliers

B. Data points with different densities

C. Data points with non-convex shapes

D. All of the above

6. For clustering, we do not require-

A. Labeled data

B. Unlabeled data

C. Numerical data

D. Categorical data

7. Which of the following is an application of clustering?

A. Biological network analysis

B. Market trend prediction

C. Topic modeling

D. All of the above

8. On which data type, we can not perform cluster analysis?

A. Time series data

B. Text data

C. Multimedia data

D. None

9. Netflix’s movie recommendation system uses-

A. Supervised learning

B. Unsupervised learning

C. Reinforcement learning

D. All of the above

10. The final output of Hierarchical clustering is-

A. The number of cluster centroids

B. The tree representing how close the data points are to each other

C. A map defining the similar data points into individual groups

D. All of the above

11. Which of the step is not required for K-means clustering?

A. a distance metric

B. initial number of clusters

C. initial guess as to cluster centroids

D. None

12. Which is the following is wrong?

A. k-means clustering is a vector quantization method

B. k-means clustering tries to group n observations into k clusters

C. k-nearest neighbor is same as k-means

D. None

13. Which of the following uses merging approach?

A. Hierarchical clustering

B. Partitional clustering

C. Density-based clustering

D. All of the above

14. Which of the following is a method of choosing the optimal number of clusters for k-means?

A. cross-validation

B. the silhouette method

C. the elbow method

D. All of the above

15. When does k-means clustering stop creating or optimizing clusters?

A. After finding no new reassignment of data points

B. After the algorithm reaches the defined number of iterations

C. Both A and B

D. None

16. Which of the following clustering algorithm follows a top to bottom approach?

A. K-means

B. Divisible

C. Agglomerative

D. None

17. Which algorithm does not require a dendrogram?

A. K-means

B. Divisible

C. Agglomerative

D. All of the above

18. Which of the following clustering algorithms suffers from the problem of convergence at local optima?

A. Takes each data point as an individual cluster

B. Goes on making clusters until it reaches to an optimal number of cluster

C. Follows a top to bottom approach

D. All of the above

19. For topic modeling what should we use?

A. Random forest

B. Support vector machine

C. K-means

D. K-nearest neighbors

20. What is a dendrogram?

A. A hierarchical structure

B. A diagram structure

C. A graph structure

D. None

Quiz Category

Machine Learning

Artificial Intelligence

Quiz Topic - Clustering