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Perform K-means Clustering | Basic Clustering Algorithms
Cluster Analysis
course content

Зміст курсу

Cluster Analysis

Cluster Analysis

1. What is Clustering?
2. Basic Clustering Algorithms
3. How to choose the best model?

bookPerform K-means Clustering

Завдання
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 desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 2. Розділ 2
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bookPerform K-means Clustering

Завдання
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 desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 2. Розділ 2
toggle bottom row

bookPerform K-means Clustering

Завдання
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 desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Завдання
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 desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 2. Розділ 2
Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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