Master Cluster Analysis Techniques for Data-Driven Insights

Question 1

Which of the following is NOT a hierarchical clustering method?

Accepted Answer

K-means clustering

Answer

Complete linkage

Answer

Ward's method

Question 2

Which clustering algorithm is NOT categorized as hierarchical?

Accepted Answer

K-means clustering

Answer

Ward's method

Answer

Complete linkage

Answer

Average linkage

Question 3

Which of the following is NOT a type of distance measure used in hierarchical clustering?

Accepted Answer

Cosine similarity

Answer

Euclidean distance

Answer

Manhattan distance

Answer

Jaccard coefficient

Question 4

Which of the following is NOT a benefit of using cluster analysis?

Accepted Answer

It can be used to generate predictions about future data points.

Answer

It can help to identify patterns and relationships in data.

Answer

It can be used to reduce the dimensionality of data.

Answer

It can be used to create a hierarchical structure of data.

Question 5

Which of the following is NOT a similarity measure commonly employed in cluster analysis?

Accepted Answer

Coefficient of variation

Answer

Euclidean distance

Answer

Manhattan distance

Answer

Mahalanobis distance

Question 6

What is the fundamental distinction between hierarchical and k-means clustering algorithms?

Accepted Answer

Hierarchical clustering generates a tree-like structure, whereas k-means clustering produces distinct, non-overlapping clusters.

Answer

Hierarchical clustering is deterministic, while k-means clustering involves probabilistic elements.

Answer

Hierarchical clustering necessitates pre-specifying the number of clusters, unlike k-means clustering.

Answer

Hierarchical clustering outperforms k-means clustering in terms of efficiency for large datasets.

Question 7

What is the primary objective of the Elbow method in k-means clustering?

Accepted Answer

Determining the optimal number of clusters.

Answer

Identifying data outliers.

Answer

Enhancing the convergence rate of the algorithm.

Answer

Visualizing the distribution of clusters.

Question 8

What is the role of dendrograms in hierarchical clustering?

Accepted Answer

Visualizing the hierarchical structure of the clusters.

Answer

Determining the optimal number of clusters.

Answer

Measuring the similarity between clusters.

Answer

Identifying the linkage method employed.

Question 9

Which of the following is NOT a criterion used to evaluate the effectiveness of a clustering solution?

Accepted Answer

Algorithmic complexity

Answer

Internal validity

Answer

External validity

Answer

Interpretability

Question 10

What is the purpose of silhouette analysis in cluster analysis?

Accepted Answer

Assessing the separation and compactness of clusters.

Answer

Identifying outliers in the data.

Answer

Determining the optimal number of clusters.

Answer

Visualizing the distribution of clusters.

Question 11

In cluster analysis, what is the main objective?

Accepted Answer

To identify natural groupings within a dataset based on similarity

Answer

To determine the number of clusters in a dataset

Answer

To identify outliers in a dataset

Answer

To perform statistical inference on cluster membership

Question 12

Which of the following is a hierarchical clustering algorithm?

Accepted Answer

Ward's method

Answer

K-means clustering

Answer

DBSCAN

Answer

Spectral clustering

Question 13

What is the primary goal of the k-means clustering algorithm?

Accepted Answer

To group data into k clusters such that the sum of squared distances from each point to its cluster centroid is minimized

Answer

To identify the smallest possible number of clusters in a dataset

Answer

To determine the most representative observations within each cluster

Answer

To optimize the separation between clusters

Question 14

What is the primary use of a dendrogram in hierarchical clustering?

Accepted Answer

To visualize the relationships between clusters

Answer

To determine the optimal number of clusters

Answer

To identify outliers in the dataset

Answer

To perform statistical inference on cluster membership

Question 15

Which of the following is NOT a commonly used cluster analysis algorithm?

Accepted Answer

Principal component analysis

Answer

Hierarchical clustering

Answer

K-means clustering

Answer

DBSCAN

Question 16

Which of the following is not a major type of clustering algorithm?

Accepted Answer

Principal component analysis

Answer

Hierarchical clustering

Answer

K-means clustering

Answer

Density-based spatial clustering of applications with noise (DBSCAN)

Question 17

What is the goal of hierarchical clustering?

Accepted Answer

To create a hierarchy of nested clusters, representing the relationships between data points.

Answer

To identify the optimal number of clusters in the data.

Answer

To find the centroids of each cluster.

Answer

To minimize the within-cluster variance.

Question 18

What is a major disadvantage of k-means clustering?

Accepted Answer

It can be sensitive to the initial choice of cluster centroids.

Answer

It assumes that the data is spherical.

Answer

It is only suitable for small datasets.

Answer

It cannot handle categorical data.

Question 19

Which of the following is an example of a non-hierarchical clustering algorithm?

Accepted Answer

K-means clustering

Answer

Ward's method

Answer

Complete-linkage clustering

Answer

Average-linkage clustering

Question 20

Which of the following factors should not be considered when choosing a clustering algorithm?

Accepted Answer

The type of computer available

Answer

The size of the dataset

Answer

The type of data

Answer

The desired level of interpretability

Question 21

Which of the following is NOT a type of agglomerative hierarchical clustering algorithm?

Accepted Answer

K-means

Answer

Single linkage

Answer

Complete linkage

Answer

Average linkage

Question 22

What is the objective of using k-means clustering?

Accepted Answer

To find the number of clusters that minimises the within-cluster sum of squares

Answer

To find the number of clusters that maximises the between-cluster sum of squares

Answer

To find the number of clusters that is equal to the number of observations

Answer

To find the number of clusters that is specified by the user

Question 23

Which of the following measures of similarity is used in single linkage hierarchical clustering?

Accepted Answer

Minimum distance between any two observations in the clusters

Answer

Euclidean distance

Answer

Mahalanobis distance

Answer

Cosine similarity

Question 24

Which of the following algorithms is an example of a non-hierarchical clustering algorithm?

Accepted Answer

K-means

Answer

Single linkage

Answer

Average linkage

Answer

Complete linkage

Question 25

Which hierarchical clustering algorithm uses the average distance between all pairs of observations in a cluster?

Accepted Answer

Average Linkage

Answer

Single Linkage

Answer

Complete Linkage

Answer

Ward's method

Question 26

Which similarity measure is based on the number of common features between two objects?

Accepted Answer

Jaccard similarity

Answer

Cosine similarity

Answer

Spearman's rank correlation

Answer

Pearson's correlation coefficient

Question 27

The dendrogram graphically represents the results of:

Accepted Answer

Hierarchical clustering

Answer

K-means clustering

Answer

DBSCAN

Answer

Density-based spatial clustering of applications with noise

Question 28

Why is data normalization crucial before applying clustering algorithms?

Accepted Answer

It ensures that all features have a similar range, making them comparable.

Answer

It reduces the dimensionality of the data, speeding up the clustering process.

Answer

It is a prerequisite for hierarchical clustering but not for k-means clustering.

Question 29

Which of the following distance measures is most appropriate for categorical data?

Accepted Answer

Jaccard distance

Answer

Euclidean distance

Answer

Mahalanobis distance

Answer

Manhattan distance

Question 30

What is the purpose of the elbow method in k-means clustering?

Accepted Answer

To determine the optimal number of clusters.

Answer

To initialize the cluster centroids.

Answer

To identify outliers in the data.

Answer

To measure the quality of a clustering solution.

Question 31

Which of the following is a type of divisive hierarchical clustering algorithm?

Accepted Answer

DIANA

Answer

Birch

Answer

DBSCAN

Answer

Spectral clustering

Question 32

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

Accepted Answer

Divisive methods start with a single cluster and split it into smaller clusters, while agglomerative methods start with individual points and merge them into larger clusters.

Answer

Divisive methods are more efficient than agglomerative methods.

Question 33

Which of the following is a limitation of cluster analysis?

Accepted Answer

It can be difficult to interpret the results.

Answer

It is computationally expensive.

Answer

It always finds the true structure in the data.

Answer

It can only be used with certain types of data.

Question 34

What is the primary purpose of the Silhouette coefficient?

Accepted Answer

Assessing the quality of a clustering solution.

Answer

Determining the optimal number of clusters.

Answer

Measuring the distance between clusters.

Answer

Initializing the cluster centroids.

Question 35

What is the fundamental goal of cluster analysis?

Accepted Answer

To group similar data points together.

Answer

To identify outliers in the data.

Answer

To predict the value of a target variable.

Question 36

Which of the following is NOT a hierarchical clustering method?

Accepted Answer

K-means

Answer

Ward's method

Answer

Average linkage

Answer

Single linkage

Question 37

What is the objective of cluster analysis?

Accepted Answer

To group data points into clusters based on their similarities or relatedness

Answer

To reduce the dimensionality of data

Answer

To predict future outcomes

Question 38

Which of the following is an advantage of cluster analysis?

Accepted Answer

It can reveal patterns and relationships in data that may not be apparent from other methods, aiding in data exploration and understanding.

Answer

It is always accurate and reliable

Answer

It is computationally efficient for all datasets

Question 39

Which of the following is a potential limitation of cluster analysis?

Accepted Answer

It can be sensitive to the choice of distance measure, clustering algorithm, and initialization parameters, potentially affecting the resulting clusters.

Answer

It always produces optimal clusters

Answer

It is not suitable for large datasets

Question 40

What is the centroid of a cluster in k-means clustering?

Accepted Answer

The average value of the data points within the cluster

Answer

The closest data point to the cluster center

Answer

The farthest data point from the cluster center

Question 41

Which of the following is a dendrogram?

Accepted Answer

A tree-like diagram that represents the hierarchical structure of clusters

Answer

A table of the cluster assignments

Answer

A scatterplot of the data points