Python for Machine Learning - The Complete Beginners Course - Density-Based Clustering

Python for Machine Learning - The Complete Beginners Course - Density-Based Clustering

Assessment

Interactive Video

Information Technology (IT), Architecture

University

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

The video tutorial explains the Dbscan algorithm, a density-based clustering method that identifies clusters of arbitrary shapes and is robust to noise. It operates using two parameters: radius (epsilon) and minimum points (M). The algorithm picks a point and checks if there are at least M points within the radius of epsilon to form a cluster. Dbscan can discover clusters surrounded by different clusters and is effective in noise detection.

Read more

5 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary characteristic of the DBSCAN algorithm?

It is a density-based clustering method.

It is a partitioning clustering method.

It is a hierarchical clustering method.

It is a grid-based clustering method.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which parameter in DBSCAN defines the radius for identifying dense areas?

Theta

Delta

Gamma

Epsilon

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the parameter 'M' represent in the DBSCAN algorithm?

Maximum distance between points

Median number of points in a cluster

Minimum number of data points in a neighborhood

Mean distance between clusters

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does DBSCAN determine if a point belongs to a cluster?

By checking if it is the centroid of a cluster

By comparing it to a predefined cluster shape

By verifying if it is within a specified distance from the origin

By ensuring it is within a radius of epsilon with at least M points

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following is an advantage of the DBSCAN algorithm?

It is not robust to noise.

It is sensitive to the initial choice of points.

It requires a predefined number of clusters.

It can discover clusters of arbitrary shapes.

Access all questions and much more by creating a free account

Create resources

Host any resource

Get auto-graded reports

Google

Continue with Google

Email

Continue with Email

Classlink

Continue with Classlink

Clever

Continue with Clever

or continue with

Microsoft

Microsoft

Apple

Apple

Others

Others

Already have an account?