Hierarchical Clustering Quiz Questions

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Hierarchical Clustering Quiz Questions

1. What are the two main types of hierarchical clustering?

A. K-means and K-medoids

B. Agglomerative and divisive

C. Density-based and grid-based

D. Model-based and graph-based

2. In agglomerative hierarchical clustering, what does the algorithm begin with?

A. Each data point in a separate cluster

B. All data points in one cluster

C. A predefined number of clusters

D. The optimal number of clusters

3. In divisive hierarchical clustering, what does the algorithm begin with?

A. Each data point in a separate cluster

B. All data points in one cluster

C. A predefined number of clusters

D. The optimal number of clusters

4. What is a dendrogram?

A. A diagram that represents the tree structure of hierarchical clustering

B. A diagram that represents the optimal number of clusters

C. A diagram that represents the density of clusters

D. A diagram that represents the distance between clusters

5. What is the purpose of a linkage function in hierarchical clustering?

A. To determine the distance between data points

B. To determine the distance between clusters

C. To determine the optimal number of clusters

D. To determine the clustering algorithm to use

6. What is single linkage in hierarchical clustering?

A. The minimum distance between data points in two clusters

B. The maximum distance between data points in two clusters

C. The average distance between data points in two clusters

D. The centroid distance between two clusters

7. What is complete linkage in hierarchical clustering?

A. The minimum distance between data points in two clusters

B. The maximum distance between data points in two clusters

C. The average distance between data points in two clusters

D. The centroid distance between two clusters

8. What is average linkage in hierarchical clustering?

A. The minimum distance between data points in two clusters

B. The maximum distance between data points in two clusters

C. The average distance between data points in two clusters

D. The centroid distance between two clusters

9. How is the optimal number of clusters determined in hierarchical clustering?

A. By maximizing the within-cluster sum of squares

B. By minimizing the between-cluster sum of squares

C. By examining the dendrogram and selecting an appropriate cut-off point

D. By using the elbow method on the resulting tree structure

10. What is the main advantage of hierarchical clustering over K-means clustering?

A. It does not require specifying the number of clusters in advance

B. It is more computationally efficient

C. It is less sensitive to the initial placement of centroids

D. It can handle categorical data

11. Which of the following is a limitation of hierarchical clustering?

A. Sensitivity to the choice of linkage function

B. Inability to handle large datasets

C. Inability to undo previous steps

D. All of the above

12. Which of the following is NOT a distance metric used in hierarchical clustering?

A. Euclidean distance

B. Manhattan distance

C. Cosine similarity

D. Pearson correlation coefficient

13. In hierarchical clustering, which of the following techniques can be used to handle categorical data?

A. Gower distance

B. One-hot encoding

C. Standardization

D. All of the above

14. What is Ward's method in hierarchical clustering?

A. A linkage method that minimizes the total within-cluster variance

B. A linkage method that maximizes the total between-cluster variance

C. A linkage method that minimizes the average distance between clusters

D. A linkage method that maximizes the centroid distance between clusters

15. Can hierarchical clustering be used for outlier detection?

A. Yes, by identifying small clusters or isolated data points in the dendrogram

B. Yes, by identifying data points with a high silhouette score

C. No, hierarchical clustering is not suitable for outlier detection

D. No, hierarchical clustering can only be used for dimensionality reduction

16. Which of the following is a disadvantage of hierarchical clustering compared to K-means clustering?

A. Hierarchical clustering is more sensitive to outliers

B. Hierarchical clustering requires specifying the number of clusters in advance

C. Hierarchical clustering is less interpretable

D. Hierarchical clustering is more computationally expensive

17. How is the cophenetic correlation coefficient used in hierarchical clustering?

A. To measure the agreement between the original distances between data points and the distances represented in the dendrogram

B. To measure the compactness of clusters

C. To measure the separation between clusters

D. To determine the optimal number of clusters

18. Which of the following is NOT a common stopping criterion for hierarchical clustering?

A. A predetermined number of clusters is reached

B. The minimum inter-cluster distance exceeds a threshold

C. The maximum intra-cluster distance falls below a threshold

D. The total within-cluster sum of squares is minimized

19. What is the primary difference between bottom-up and top-down hierarchical clustering?

A. Bottom-up starts with each data point in a separate cluster, while top-down starts with all data points in a single cluster

B. Bottom-up is a deterministic approach, while top-down is a stochastic approach

C. Bottom-up is used for continuous data, while top-down is used for categorical data

D. Bottom-up is a density-based approach, while top-down is a distance-based approach

20. Which distance metric is more appropriate for high-dimensional data in hierarchical clustering?

A. Euclidean distance

B. Manhattan distance

C. Cosine similarity

D. Pearson correlation coefficient

21. Can hierarchical clustering handle missing data?

A. Yes, by using imputation methods to fill in missing values

B. Yes, by using distance metrics that can handle missing data

C. No, hierarchical clustering requires complete data

D. No, hierarchical clustering is only suitable for categorical data

22. What is the primary advantage of using hierarchical clustering for time series data?

A. It can handle missing data

B. It does not require specifying the number of clusters in advance

C. It can detect seasonality and trends in the data

D. It can capture the temporal structure of the data

23. How does dynamic time warping (DTW) distance differ from other distance metrics in hierarchical clustering for time series data?

A. DTW distance is invariant to time shifts and scaling

B. DTW distance is sensitive to noise

C. DTW distance is only suitable for continuous data

D. DTW distance is a similarity measure rather than a distance metric

24. How can hierarchical clustering be used for feature selection?

A. By using the silhouette score to rank the importance of features

B. By examining the dendrogram to identify redundant features

C. By selecting features that minimize the total within-cluster variance

D. By clustering features and selecting representative features from each cluster

25. What is the primary goal of cluster validation in hierarchical clustering?

A. To determine the optimal number of clusters

B. To assess the quality and stability of the clustering solution

C. To identify the best linkage function for the dataset

D. To determine the most appropriate distance metric for the dataset

26. What is the silhouette score in hierarchical clustering?

A. A measure of the agreement between the original distances between data points and the distances represented in the dendrogram

B. A measure of the compactness of clusters

C. A measure of the separation between clusters

D. A measure of both the compactness and separation of clusters

27. Can hierarchical clustering be applied to text data?

A. Yes, by converting text data into numerical representations, such as term frequency-inverse document frequency (TF-IDF) vectors

B. Yes, by using cosine similarity as the distance metric

C. No, hierarchical clustering is not suitable for text data

D. No, hierarchical clustering can only be applied to continuous data

28. What is the main difference between hierarchical clustering and partitional clustering methods?

A. Hierarchical clustering does not require specifying the number of clusters in advance, while partitional clustering does

B. Hierarchical clustering uses a tree structure to represent the relationships between clusters, while partitional clustering does not

C. Hierarchical clustering can handle missing data, while partitional clustering cannot

D. Hierarchical clustering is deterministic, while partitional clustering is stochastic

29. How can hierarchical clustering be used for dimensionality reduction?

A. By applying the clustering algorithm to the features instead of the data points

B. By applying the clustering algorithm to the data points and using the cluster centroids as a reduced feature space

C. By applying the clustering algorithm to the data points and using the cluster labels as a reduced feature space

D. By applying the clustering algorithm to the data points and using the dendrogram to guide the selection of a reduced feature space

30. Which of the following methods is NOT a hierarchical clustering algorithm?

A. Agglomerative clustering

B. Divisive clustering

C. K-means clustering

D. Ward's method

Quiz Category

Machine Learning

Hierarchical Clustering Quiz Questions