Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Perform K-means Clustering | Basic Clustering Algorithms
Cluster Analysis
course content

Course Content

Cluster Analysis

Cluster Analysis

1. What is Clustering?
Formulation Of The ProblemWhat Is Unique About Clustering?Types Of Clustering
2. Basic Clustering Algorithms
K-means Clustering Perform K-means ClusteringAgglomerative ClusteringPerform Agglomerative ClusteringMean Shift Clustering DBSCAN ClusteringPerform DBSCAN ClusteringUsing Clustering On Real Data
3. How to choose the best model?
Internal EvaluationExternal EvaluationQuality Evaluation

bookPerform K-means Clustering

Task
test

Swipe to show code editor

Let's check the efficiency of the algorithm on different types of clusters. Now we will use the three built-in datasets of the sklearn library and try to use the K-means algorithm to cluster the corresponding points. We will provide visualizations and try to estimate the quality of clustering using these visualizations.

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

  1. Use KMeans class from cluster module for import.
  2. Use KMeans class to instantiate a class object
  3. Use.fit()method to train model.
  4. Use .labels_attribute to extract fitted clusters.

Once you've completed this task, click the button below the code to check your solution.

Note

In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

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!

Section 2. Chapter 2
toggle bottom row

bookPerform K-means Clustering

Task
test

Swipe to show code editor

Let's check the efficiency of the algorithm on different types of clusters. Now we will use the three built-in datasets of the sklearn library and try to use the K-means algorithm to cluster the corresponding points. We will provide visualizations and try to estimate the quality of clustering using these visualizations.

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

  1. Use KMeans class from cluster module for import.
  2. Use KMeans class to instantiate a class object
  3. Use.fit()method to train model.
  4. Use .labels_attribute to extract fitted clusters.

Once you've completed this task, click the button below the code to check your solution.

Note

In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

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!

Section 2. Chapter 2
toggle bottom row

bookPerform K-means Clustering

Task
test

Swipe to show code editor

Let's check the efficiency of the algorithm on different types of clusters. Now we will use the three built-in datasets of the sklearn library and try to use the K-means algorithm to cluster the corresponding points. We will provide visualizations and try to estimate the quality of clustering using these visualizations.

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

  1. Use KMeans class from cluster module for import.
  2. Use KMeans class to instantiate a class object
  3. Use.fit()method to train model.
  4. Use .labels_attribute to extract fitted clusters.

Once you've completed this task, click the button below the code to check your solution.

Note

In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

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!

Task
test

Swipe to show code editor

Let's check the efficiency of the algorithm on different types of clusters. Now we will use the three built-in datasets of the sklearn library and try to use the K-means algorithm to cluster the corresponding points. We will provide visualizations and try to estimate the quality of clustering using these visualizations.

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

  1. Use KMeans class from cluster module for import.
  2. Use KMeans class to instantiate a class object
  3. Use.fit()method to train model.
  4. Use .labels_attribute to extract fitted clusters.

Once you've completed this task, click the button below the code to check your solution.

Note

In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt