Master Cluster Analysis Techniques for Market Segmentation

Question 1

What is the Elbow Method primarily used for in k-means clustering?

Accepted Answer

Determining the optimal number of clusters

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Evaluating the clustering's accuracy

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Calculating distances between clusters

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Identifying outliers within the data

Question 2

What is the primary reason for employing a distance matrix in cluster analysis?

Accepted Answer

Calculating the distance between each pair of data points

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Storing the clustering algorithm's results

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Visualizing data in a two-dimensional space

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Identifying outliers within the data

Question 3

In hierarchical clustering, the Euclidean distance between two observations is calculated as:

Accepted Answer

The square root of the sum of the squared differences between their values on all attributes

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The sum of the absolute differences between their values on all attributes

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The correlation coefficient between their values on all attributes

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The cosine similarity between their values on all attributes

Question 4

When choosing the optimal number of clusters in k-means clustering, the AIC (Akaike Information Criterion) is:

Accepted Answer

A criterion used to evaluate the goodness of fit of a model

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A measure of the number of parameters in a model

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A measure of the variance within clusters

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A measure of the distance between clusters

Question 5

Which of the following applications is most appropriate for hierarchical clustering?

Accepted Answer

Market segmentation

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Customer churn prediction

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Image recognition

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Natural language processing

Question 6

External cluster validation indices, compared to internal indices, are advantageous because they:

Accepted Answer

Evaluate clustering solutions based on external data, providing a more objective assessment

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Are more computationally efficient

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Can be used to compare different clustering algorithms

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Are more robust to noise and outliers

Question 7

A benefit of using hierarchical clustering over k-means clustering is that it:

Accepted Answer

Can produce a hierarchy of clusters, allowing for more flexibility in analysis

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Can handle data with different scales

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Is more robust to outliers

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Is more computationally efficient

Question 8

The primary purpose of a dendrogram in hierarchical clustering is to:

Accepted Answer

Visualize the hierarchical structure of the clusters

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Determine the optimal number of clusters

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Assign new observations to clusters

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Compute the distance between clusters

Question 9

What is the primary goal of market segmentation using clustering techniques?

Accepted Answer

Identification of potential customers for specific offerings

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Enhancement of customer satisfaction

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Reduction of marketing expenses

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Increase in brand awareness

Question 10

What is the purpose of a dendrogram in hierarchical clustering?

Accepted Answer

To visually represent the hierarchical structure of the clusters

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To determine the optimal number of clusters

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To compute the distance between clusters

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To assign data points to clusters

Question 11

Which option is a type of hierarchical clustering technique?

Accepted Answer

Single Linkage

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K-Means

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Principal Component Analysis

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Logistic Regression

Question 12

What is the primary aim of k-means clustering?

Accepted Answer

Grouping data points into clusters based on their similarity

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Reducing the dimensionality of data

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Identifying outliers in data

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Predicting future values of a variable

Question 13

What's the main use of a dendrogram in hierarchical clustering?

Accepted Answer

Visualizing the hierarchical structure of clusters

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Determining the optimal number of clusters

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Evaluating the performance of clustering algorithms

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Comparing different clustering techniques

Question 14

Which of the following is a limitation of hierarchical clustering?

Accepted Answer

Assumes that the data is hierarchical in nature

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Can handle large datasets

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Provides a visual representation of the data structure

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Can detect clusters of different shapes and sizes

Question 15

What is the primary difference between hierarchical and k-means clustering?

Accepted Answer

Hierarchical clustering creates a tree-like structure, while k-means clustering assigns data points to predefined clusters

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Hierarchical clustering can handle missing data, while k-means clustering cannot

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K-means clustering is deterministic, while hierarchical clustering is not

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Hierarchical clustering is more computationally efficient than k-means clustering

Question 16

What is the purpose of the 'k' parameter in k-means clustering?

Accepted Answer

Specifies the number of clusters to be created

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Determines the distance measure to be used

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Sets the maximum number of iterations for the algorithm

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Initializes the centroids of the clusters

Question 17

What is the distinction between agglomerative and divisive hierarchical clustering algorithms?

Accepted Answer

Agglomerative algorithms start with individual data points and merge them into clusters, while divisive algorithms start with one large cluster and split it into smaller clusters

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Agglomerative algorithms are more efficient than divisive algorithms

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Divisive algorithms can handle larger datasets than agglomerative algorithms

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There is no fundamental difference between agglomerative and divisive hierarchical clustering algorithms

Question 18

Which of the following is a fundamental principle of hierarchical clustering?

Accepted Answer

Starting with individual data points and iteratively merging them into clusters

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Determining an optimal number of clusters using k-means optimization

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Reducing dimensionality through principal component analysis

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Predicting class labels using Bayesian classification

Question 19

What is the key objective in k-means clustering?

Accepted Answer

Minimizing the sum of squared distances between data points and their assigned cluster centroids

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Maximizing the ratio of inter-cluster variance to intra-cluster variance

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Identifying the smallest number of clusters that explain a given proportion of data variance

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Finding the most distinct clusters in a dataset

Question 20

What is the primary purpose of using the Silhouette coefficient in cluster analysis?

Accepted Answer

Evaluating the overall quality and separation of a clustering solution

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Assessing the similarity of data points within a cluster

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Determining the optimal number of clusters to form

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Visualizing the distribution of clusters

Question 21

Which distance measure is commonly used in hierarchical clustering to determine the similarity between two clusters, where the distance between two clusters is defined as the minimum distance between any two points from the two clusters?

Accepted Answer

Single linkage

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Mahalanobis distance

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Euclidean distance

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Cosine similarity

Question 22

In k-means clustering, which initialization method selects k data points randomly and assigns them as the initial cluster centroids?

Accepted Answer

Random initialization

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K-medoids initialization

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Hierarchical initialization

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Farthest-first traversal

Question 23

Which of the following is NOT a commonly used criterion for evaluating the performance of clustering algorithms?

Accepted Answer

Interpretability

Answer

Davies-Bouldin index

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Silhouette coefficient

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Calinski-Harabasz index

Question 24

Which assumption underlies hierarchical clustering algorithms?

Accepted Answer

Data points within a cluster are similar to each other

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The data is normally distributed

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Data points are distributed evenly across the feature space

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The number of clusters is known in advance

Question 25

In k-means clustering, what is the purpose of the centroid of a cluster?

Accepted Answer

To represent the center of the cluster

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To assign data points to clusters

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To determine the number of clusters

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To assess the quality of clustering

Question 26

Which of the following clustering algorithms is particularly suitable for handling high-dimensional data with complex shapes?

Accepted Answer

Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Answer

Fuzzy c-means clustering

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K-medoids clustering

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Hierarchical clustering

Question 27

In hierarchical clustering, which method uses the average distance between all pairs of points from two clusters as the linkage criterion?

Accepted Answer

Average linkage

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Complete linkage

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Single linkage

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Ward's minimum variance method