Introduction to Machine Learning Quiz

How k-means clustering works?

• Initialization

• Assignment

• Update centroids

• Repeat

What is the goal of clustering?

• To identify patterns and relationships in data

• To classify data points

• To label data points

• To visualize data

What does K define in K-Means Clustering?

• The number of clusters

• The number of data points

• The number of iterations

• The number of features

What is the first step in the K-Means Algorithm?

• Select random K points

• Assign data points to clusters

• Calculate the variance

• Select the number K

What is a Dendrogram?

• A diagram that shows the hierarchical relationship between objects.

• A method for splitting a cluster.

• A type of clustering algorithm.

• A measure of dissimilarity between clusters.

What is the difference between Agglomerative and Divisive clustering?

• Agglomerative is a bottom-up approach, while Divisive is a top-down approach.

• Agglomerative requires prespecifying the number of clusters, while Divisive does not.

• Agglomerative merges clusters, while Divisive splits them.

• Both approaches are the same.

What is Single Linkage?

• The minimum distance between the members of two clusters.

• The maximum distance between the members of two clusters.

• The average distance between the members of two clusters.

• A method for splitting clusters.

What is Complete Linkage?

• The maximum distance between the members of two clusters.

• The minimum distance between the members of two clusters.

• The average distance between the members of two clusters.

• A method for merging clusters.

What is Average Linkage?

• The average of all distances between members of two clusters.

• The minimum distance between the members of two clusters.

• The maximum distance between the members of two clusters.

• A method for clustering.

What is Density-Based Clustering?

• A methodology used in model building and machine learning algorithms.

• A method for hierarchical clustering.

• A type of supervised learning.

• A clustering method that requires prespecifying the number of clusters.