Sunday, February 19, 2023

Machine Learning MCQ - Single linkage and complete linkage hierarchical clustering

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, hierarchical clustering, agglomerative clustering, single linkage cluster distance, complete linkage cluster distance, single-link vs complete-link distance calculations

Machine Learning MCQ - Distance between points in single linkage and complete linkage hierarchical clustering methods

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1. Considering single-link and complete-link hierarchical clustering, is it possible for a point to be closer to the points in other clusters than to the points in its own cluster? If so, in which approach will this tend to be observed?

a) No

b) Yes, single-link clustering

c) Yes, complete-link clustering

d) Yes, both single-link and complete-link clustering

 

Answer: (d) Yes, both single-link and complete-link clustering

This is possible in both single-link and complete-link clustering. In the single-link case, an example would be two parallel chains where many points are closer to points in the other chain/cluster than to points in their own cluster. In the complete-link case, this notion is more intuitive due to the clustering constraint (measuring distance between two clusters by the distance between their farthest points).

 

What is single link clustering?

Single link clustering is one of the hierarchical clustering methods.

In single linkage (i.e., nearest-neighbor linkage or sometimes referred as MIN), the dissimilarity between two clusters is the smallest dissimilarity between two points in
opposite groups.

In other words, in single linkage clustering, the inter-cluster distance (the distance between two clusters) is represented by the
distance of the closest pair of data objects belonging to
different clusters.

cluster distance = distance of two closest members in each class

 

What is complete link clustering?

Yet another hierarchical clustering method.

In complete linkage (i.e., furthest-neighbor linkage or MAX), dissimilarity
between two clusters is the largest dissimilarity between two points in
opposite groups.

In other words, in complete linkage clustering, the inter-cluster distance (the distance between two clusters) is represented by the
distance of the farthest pair of data objects belonging to
different clusters.

cluster distance = distance of two farthest members

 

What is the term linkage refers to in hierarchical clustering?

 The choice of linkage determines how we measure dissimilarity between groups of points.

 

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Related links:

What is hierarchical clustering?

Difference between single-linkage and complete-linkage hierarchical clustering in ML

What is single link clustering?

What is complete link clustering?

What does linkage refer to in hierarchical agglomerative clustering?

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