Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Learn Perform DBSCAN Clustering | Basic Clustering Algorithms
Cluster Analysis

bookPerform DBSCAN Clustering

Task

Swipe to start coding

As we mentioned in the previous chapter, DBSCAN algorithm classifies points as core, border, and noise. As a result, we can use this algorithm to clean our data from outliers. Let's create DBSCAN model, clean data, and look at the results.

Your task is to train DBSCAN model on the circles dataset, detect noise points, and remove them. Look at the visualization and compare data before and after cleaning. You have to:

  1. Import the DBSCAN class from sklearn.cluster module.
  2. Use DBSCAN class and .fit() method of this class.
  3. Use .labels_ attribute of DBSCAN class.
  4. Specify clustering.labels_==-1 to detect noise.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 2. ChapterΒ 7
single

single

Ask AI

expand

Ask AI

ChatGPT

Ask anything or try one of the suggested questions to begin our chat

close

Awesome!

Completion rate improved to 7.14

bookPerform DBSCAN Clustering

Task

Swipe to start coding

As we mentioned in the previous chapter, DBSCAN algorithm classifies points as core, border, and noise. As a result, we can use this algorithm to clean our data from outliers. Let's create DBSCAN model, clean data, and look at the results.

Your task is to train DBSCAN model on the circles dataset, detect noise points, and remove them. Look at the visualization and compare data before and after cleaning. You have to:

  1. Import the DBSCAN class from sklearn.cluster module.
  2. Use DBSCAN class and .fit() method of this class.
  3. Use .labels_ attribute of DBSCAN class.
  4. Specify clustering.labels_==-1 to detect noise.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 2. ChapterΒ 7
single

single

close

Awesome!

Completion rate improved to 7.14

bookPerform DBSCAN Clustering

Swipe to show menu

Task

Swipe to start coding

As we mentioned in the previous chapter, DBSCAN algorithm classifies points as core, border, and noise. As a result, we can use this algorithm to clean our data from outliers. Let's create DBSCAN model, clean data, and look at the results.

Your task is to train DBSCAN model on the circles dataset, detect noise points, and remove them. Look at the visualization and compare data before and after cleaning. You have to:

  1. Import the DBSCAN class from sklearn.cluster module.
  2. Use DBSCAN class and .fit() method of this class.
  3. Use .labels_ attribute of DBSCAN class.
  4. Specify clustering.labels_==-1 to detect noise.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

some-alt