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Cluster Analysis
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Contenido del Curso

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

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 2
toggle bottom row

bookPerform K-means Clustering

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 2
toggle bottom row

bookPerform K-means Clustering

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 2. Capítulo 2
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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