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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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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Machine Learning MCQ - Best method to find optimal number of clusters (k) in k-means algorithm

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, How to find optimal k value in k-means? Elbow method vs silhouette method, Which is the best method to find optimal number of clusters in k-means?

Machine Learning MCQ - Which is the method to find optimal number of clusters (k value) in k-means algorithm?

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1. K-means is an unsupervised learning algorithm. In K-means, k refers to the number of clusters. We have several methods to find the optimal number of clusters in K-means algorithm. Which of the following methods can give optimal (best) number of clusters?

a) Manhattan method

b) Elbow method

c) Euclidean method

d) Silhouette method

Answer: (d) Silhouette method The silhouette method for finding optimal k value in k-means The silhouette Method is a method to find the optimal number of clusters and interpretation and validation of consistency within clusters of data. The silhouette method computes silhouette coefficients of each point that measure how much a point is similar to its own cluster compared to other clusters by providing a succinct graphical representation of how well each object has been classified. Compute silhouette coefficients for each of point, and average it out for all the samples to get the silhouette score. [For more please refer here]   Why elbow method is not chosen as the better method over silhouette method in finding the best value for k in k-means? Usually elbow curve method is a little ambiguous as the bend point for some datasets is not visible clearly.   Difference between Elbow and Silhouette methods Metrics Elbow method Silhouette method Calculation Calculates the Euclidean distance Considers variables such as variance, skewness, etc. Dataset size Works well for smaller datasets It is a better option for higher-dimensional data Effect of duplicate data May not give proper output in case of duplicate data available Works better and identifies duplicate data Efficacy Efficiency depends on the nature of the dataset Does not depend on the nature of the dataset Finding k The elbow method is used to find the “elbow” point, where adding additional data samples does not change cluster membership much. [Refer here for more] Silhouette score determines whether there are large gaps between each sample and all other samples within the same cluster or across different clusters.

 

  

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What metric can be used to find optimal number of clusters?

Differentiate between elbow method and silhouette method to find optimal clusters

Find optimal number of clusters using k-means algorithm

Why silhouette method is better than elbow method in finding optimal number of clusters in k-means algorithm?

Among elbow and silhouette methods, which is good if the data is of high-dimensions?

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