Why might a 4-cluster solution be rejected in practical analysis?

It may create a cluster with only one member

What is the primary goal of using cluster analysis in marketing research?

To segment customers based on behavioral patterns

What metric does the Elbow Method evaluate to determine optimal clusters?

Within-Cluster Sum of Squares (WCSS)

What does a sharp increase in the agglomeration schedule indicate?

A good place to stop merging clusters

What does a silhouette score measure in clustering evaluation?

How well-separated clusters are

How does Ward's method choose clusters to merge?

Minimizes total within-cluster variance

Why are hierarchical methods preferred for small datasets?

They provide a visual hierarchy of merges

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Advanced Cluster Analysis for Marketing Insights

10 termsStudBud Administration13 plays

This quiz challenges your understanding of high-level cluster analysis concepts, including algorithm distinctions, practical applications in marketing, and technical evaluation techniques like the Elbow Method and dendrogram interpretation.

Flashcards10 cardsFPractice quizPHost liveL

Questions 10

Answers

1
Why might a 4-cluster solution be rejected in practical analysis?
It may create a cluster with only one member
It increases total variation explained
It’s optimal for all models
It always overlaps with other clusters
2
Which linkage method uses the average distance between all pairs of points in clusters?
Ward's method
Average linkage
Single linkage
Centroid linkage
3
What is the primary goal of using cluster analysis in marketing research?
To visualize regression models
To measure central tendency
To segment customers based on behavioral patterns
To clean data before analysis
4
Which distance metric is most commonly used for numeric data in clustering?
Cosine similarity
Jaccard distance
Euclidean distance
Manhattan distance
5
What metric does the Elbow Method evaluate to determine optimal clusters?
Number of overlapping points
Within-Cluster Sum of Squares (WCSS)
External variance between clusters
Data normalization level
6
What does a sharp increase in the agglomeration schedule indicate?
The lowest silhouette score
A good place to stop merging clusters
A need to re-standardize data
The final number of variables
7
What does a silhouette score measure in clustering evaluation?
How well-separated clusters are
Distance from original mean
Cluster centroid accuracy
Variance in cluster size
8
Which algorithm adjusts centroids iteratively to group data points?
Hierarchical clustering
K-means clustering
Logistic regression
Linear discriminant analysis
9
How does Ward's method choose clusters to merge?
Minimizes total within-cluster variance
Maximizes inter-cluster distance
Uses median cluster value
Uses mean distance between all points
10
Why are hierarchical methods preferred for small datasets?
They support centroids automatically
They use fewer variables
They scale to massive datasets
They provide a visual hierarchy of merges

Advanced Cluster Analysis for Marketing Insights Flashcards | StudBud

Advanced Cluster Analysis for Marketing Insights

10 termsStudBud Administration13 plays

Flashcards10 cardsFPractice quizPHost liveL

Questions 10

Answers

1
Why might a 4-cluster solution be rejected in practical analysis?
It may create a cluster with only one member
It increases total variation explained
It’s optimal for all models
It always overlaps with other clusters
2
Which linkage method uses the average distance between all pairs of points in clusters?
Ward's method
Average linkage
Single linkage
Centroid linkage
3
What is the primary goal of using cluster analysis in marketing research?
To visualize regression models
To measure central tendency
To segment customers based on behavioral patterns
To clean data before analysis
4
Which distance metric is most commonly used for numeric data in clustering?
Cosine similarity
Jaccard distance
Euclidean distance
Manhattan distance
5
What metric does the Elbow Method evaluate to determine optimal clusters?
Number of overlapping points
Within-Cluster Sum of Squares (WCSS)
External variance between clusters
Data normalization level
6
What does a sharp increase in the agglomeration schedule indicate?
The lowest silhouette score
A good place to stop merging clusters
A need to re-standardize data
The final number of variables
7
What does a silhouette score measure in clustering evaluation?
How well-separated clusters are
Distance from original mean
Cluster centroid accuracy
Variance in cluster size
8
Which algorithm adjusts centroids iteratively to group data points?
Hierarchical clustering
K-means clustering
Logistic regression
Linear discriminant analysis
9
How does Ward's method choose clusters to merge?
Minimizes total within-cluster variance
Maximizes inter-cluster distance
Uses median cluster value
Uses mean distance between all points
10
Why are hierarchical methods preferred for small datasets?
They support centroids automatically
They use fewer variables
They scale to massive datasets
They provide a visual hierarchy of merges