Master Hierarchical Clustering: A Comprehensive Guide for Machine Learning

Question 1

Which hierarchical clustering algorithm is appropriate for handling categorical data?

Accepted Answer

Ward's minimum variance method

Answer

Complete linkage

Answer

Single linkage

Answer

Average linkage

Question 2

Which of the following is NOT a fundamental concept in hierarchical clustering?

Accepted Answer

K-means

Answer

Dendrogram

Answer

Cluster validity

Answer

Linkage method

Question 3

Which linkage method calculates the distance between clusters based on the minimum distance between any two observations in different clusters?

Accepted Answer

Single linkage

Answer

Ward's method

Answer

Complete linkage

Answer

Average linkage

Question 4

When determining the optimal number of clusters in hierarchical clustering, which factor is most crucial to consider?

Accepted Answer

Cluster cohesion and separation

Answer

Type of linkage method

Answer

Computational complexity

Answer

Size of the dataset

Question 5

What is the key difference between divisive and agglomerative hierarchical clustering?

Accepted Answer

Divisive starts with all data points in one cluster and splits it into smaller clusters, while agglomerative starts with each data point as its own cluster and merges them iteratively.

Answer

Divisive uses a bottom-up approach, while agglomerative uses a top-down approach.

Question 6

What is a major advantage of using hierarchical clustering?

Accepted Answer

It can uncover natural groupings of diverse sizes and shapes within the data.

Answer

It is exceptionally computationally efficient.

Answer

It can handle categorical data directly without requiring transformations.

Question 7

Which of these algorithms is commonly employed for hierarchical clustering?

Accepted Answer

Ward's method

Answer

Support Vector Machines

Answer

Principal Component Analysis

Answer

K-means

Question 8

What does a dendrogram visually represent in the context of hierarchical clustering?

Accepted Answer

The hierarchical relationships and the merging order of data points throughout the clustering process.

Answer

A set of rules for assigning data points to clusters.

Answer

A table of distances between data points.

Question 9

Which of the following is a potential drawback of hierarchical clustering?

Accepted Answer

It can be computationally expensive, especially when dealing with large datasets.

Answer

It requires the number of clusters to be predetermined before running the algorithm.

Answer

It can only identify clusters that are perfectly spherical in shape.

Question 10

What is the primary purpose of the cophenetic correlation coefficient in hierarchical clustering?

Accepted Answer

To assess how well the hierarchical clustering structure reflects the original distance relationships between data points.

Answer

To assess the accuracy of the clustering.

Answer

To determine the optimal number of clusters.

Question 11

Which of the following is a common practical application of hierarchical clustering?

Accepted Answer

Customer segmentation

Answer

Regression analysis

Answer

Predictive modeling

Answer

Image recognition

Question 12

What is the difference between agglomerative and divisive hierarchical clustering?

Accepted Answer

Agglomerative starts with individual data points and merges them, while divisive starts with all data points in one cluster and splits them.

Answer

Agglomerative uses a top-down approach, while divisive uses a bottom-up approach.

Question 13

Which of the following linkage criteria measures the average distance between all pairs of data points in a cluster?

Accepted Answer

Average linkage

Answer

Ward's method

Answer

Complete linkage

Answer

Single linkage

Question 14

What is the purpose of pruning in hierarchical clustering?

Accepted Answer

To remove unnecessary branches from the dendrogram and improve the interpretability of the clustering.

Answer

To determine the optimal number of clusters.

Question 15

Which of the following measures the compactness of a cluster?

Accepted Answer

Intracluster distance

Answer

Intercluster distance

Answer

Silhouette coefficient

Answer

Davies-Bouldin index

Question 16

Which of the following distance metrics is commonly used in hierarchical clustering?

Accepted Answer

Euclidean distance

Answer

Hamming distance

Answer

Manhattan distance

Answer

Cosine similarity

Question 17

Which hierarchical clustering algorithm creates a binary tree structure?

Accepted Answer

Agglomerative hierarchical clustering

Answer

Complete-linkage hierarchical clustering

Answer

Average-linkage hierarchical clustering

Answer

Divisive hierarchical clustering

Question 18

What is the purpose of the dendrogram in hierarchical clustering?

Accepted Answer

To visualize the hierarchical structure of the clusters

Answer

To calculate the distance between clusters

Answer

To determine the optimal number of clusters

Question 19

Which linkage method in hierarchical clustering results in clusters with similar densities?

Accepted Answer

Average-linkage

Answer

Complete-linkage

Answer

Ward's method

Question 20

What is the time complexity of agglomerative hierarchical clustering?

Accepted Answer

O(n^2 log n)

Answer

O(n)

Answer

O(n^2)

Answer

O(n log n)

Question 21

Which method can be used to determine the optimal number of clusters in hierarchical clustering?

Accepted Answer

Gap statistic

Answer

Silhouette coefficient

Answer

Davies-Bouldin index

Answer

Calinski-Harabasz index

Question 22

What is the main advantage of hierarchical clustering over partitional clustering?

Accepted Answer

Hierarchical structure allows for more flexibility in cluster interpretation

Answer

Hierarchical clustering is faster than partitional clustering

Answer

Hierarchical clustering can handle larger datasets than partitional clustering

Question 23

Which of the following applications is best suited for hierarchical clustering?

Accepted Answer

Customer segmentation

Answer

Natural language processing

Answer

Fraud detection

Answer

Image recognition

Question 24

What is a dendrogram in the context of hierarchical clustering?

Accepted Answer

A hierarchical representation of the distances between data points

Answer

A table that displays the clusters formed at each level

Answer

A list of the data points that belong to each cluster

Question 25

Which of the following is NOT a challenge associated with hierarchical clustering?

Accepted Answer

Determining the optimal number of clusters is straightforward

Answer

Clusters can be sensitive to changes in linkage methods

Answer

Hierarchical clustering can be computationally expensive