ML-Hierarchical Clustering

Hierarchical Clustering is a type of unsupervised machine learning algorithm used for:

• Classification

• Regression

• Clustering

• Dimensionality reduction

Which of the following describes the primary goal of Hierarchical Clustering?

• To minimize the variance within clusters

• To maximize the number of clusters

• To predict the target variable of a dataset

• To group similar data points into clusters

What is the main difference between agglomerative and divisive hierarchical clustering?

• Agglomerative clustering starts with each data point as its own cluster, while divisive clustering starts with all data points in a single cluster.

• Agglomerative clustering merges clusters in each step, while divisive clustering splits clusters in each step.

• Agglomerative clustering only works with numerical data, while divisive clustering can handle both numerical and categorical data.

• Agglomerative clustering requires the number of clusters to be specified in advance, while divisive clustering does not.

The output of hierarchical clustering is commonly visualized using a:

• Scatter plot

• Histogram

• Dendrogram

• Pie chart

In hierarchical clustering, what does the term "linkage" refer to?

• The process of associating data points with their nearest cluster centroids

• The measure used to compute the distance between clusters

• The initialization of cluster centroids

• The number of clusters to be formed

Which of the following is a linkage criterion used in hierarchical clustering to measure the distance between clusters?

• Random linkage

• Maximum linkage (Complete linkage)

• Total linkage

• Uniform linkage (Average linkage)

What does the "dendrogram" in hierarchical clustering represent?

• The final cluster centroids

• The distance between data points and cluster centroids

• The hierarchy of merged or split clusters

• The number of data points in each cluster

How are clusters merged in agglomerative hierarchical clustering?

• The two closest clusters are merged in each iteration.

• Clusters are randomly selected and merged iteratively.

• Clusters are merged based on their centroids.

• The largest and smallest clusters are merged at each step.

Which step in agglomerative hierarchical clustering involves calculating the distance between clusters?

• Initialization step

• Merging step

• Assigning step

• Linkage step

How is the distance between two clusters computed in single linkage (nearest neighbor) linkage?

• The maximum distance between any two data points in the clusters

• The mean distance between all pairs of data points in the clusters

• The minimum distance between any two data points in the clusters

• The sum of distances between all pairs of data points in the clusters