Quiz on Distance Based Linkage Methods

What is a cluster in data analysis?

• A group of data points that are similar to each other

• A single data point

• A random collection of data

• A method of data storage

Which of the following is a characteristic of clusters?

• Clusters must overlap

• Clusters can vary in size, shape, and density

• Clusters are always of equal size

• Clusters are always circular

What does hierarchical clustering involve?

• Eliminating outliers from the data

• Organizing data points into a hierarchical structure

• Grouping data points into a flat structure

• Randomly selecting data points

In single linkage, how is the distance between two clusters defined?

• The average distance between all pairs of points from the two clusters

• The maximum distance between any pair of points from the two clusters

• The distance between the centroids of the two clusters

• The minimum distance between any pair of points from the two clusters

What is the main focus of complete linkage?

• The average distance between points

• The distance between the centroids

• The maximum distance between points

• The minimum distance between points

What does average linkage return?

• The arithmetic mean of distances between points

• The distance between the centroids

• The minimum distance between points

• The maximum distance between points

Which linkage method is sensitive to noise and outliers?

• Complete Linkage

• Average Linkage

• Single Linkage

• Hierarchical Linkage

What is the first step in the single linkage method?

• Creating a flat structure

• Calculating the average distance

• Fusing clusters based on minimum distance

• Finding the maximum distance

What is the formula for single linkage distance?

• L(R,S)=average(D(i,j))

• L(R,S)=min(D(i,j))

• L(R,S)=D(i,j)

• L(R,S)=max(D(i,j))

What is the purpose of calculating distances in hierarchical clustering?

• To measure dissimilarity between sub-clusters

• To visualize data points

• To determine the best clustering method

• To eliminate outliers